# LABLOOP W17 — paper 2 complete experiments: full learning notebook

Prepared 2026-09-23 for Peter Ciancaglini. This is a study and execution guide; it does not record that you completed the work. Keep your existing notes, commitments, progress and evidence. Write actual results in the existing evidence register, not into the illustrative examples below.

[Original weekly brief](./specs.md) · [Commitment](./commitment.md) · [Evidence](./evidence.md) · [Review](./review.md) · [Notebook index and export guide](../../NOTEBOOKS.md) · [NotebookLM upload text](./notebooklm.txt)

[Previous notebook](../week-16-paper-1-preprint-gate/learning-notebook.md) · [Next notebook](../week-18-paper-2-full-draft/learning-notebook.md)

## 1. The week in plain language: what, why and where it leads

Paper 2 needs the whole planned experiment matrix, not a few attractive runs. Learning curves show how performance changes as the number of training batches grows. Repeated seeds reveal sensitivity to subset selection or randomness; they do not create new independent biological data. You will also test behavior on faulty batches, where the data distribution may differ from normal training data.

**Your systems, software and AI connection.** Experiment orchestration is close to CI and batch infrastructure: deterministic configs, status tracking, checkpoints and failure logs. Scientific fairness means all models face the same split and target. The protein exercise stays a separate, bounded preparation for Paper 3 rather than contaminating the Paper 2 results.

**Dependency map from the existing programme.**

- ← **From:** week 12 harness and hybrid v0, week 16 conformal intervals, week 4 batch-wise CV, week 3 hours-to-detection.
- → **Feeds:** week 18 (Paper 2 draft), week 20 (arXiv), weeks 21-22 (the Meltome baseline you start today becomes Paper 3's baseline table).
- ⟳ **Returns in:** Paper 2 figures; JOSS (wk 36) as the showcase usage of batchlens.

**Gate and proof required by the current weekly specification.**

This is an enabling week. Its deliverables feed the next scheduled gate: 20. Complete the acceptance checklist below; do not promote reputation from a draft.

The milestones are targets, not predictions of success. A prepared notebook, generated visual, course completion or draft paper is not a substitute for the required evidence. Your final profile should connect a clear scientific question to working software, trustworthy data, measured comparisons, documented limitations and your own contribution.

## 2. Before starting: locate inputs and repair only the needed foundations

- [Week 12](../week-12-endorsement-and-hybrid-model/specs.md): inspect its actual evidence and review; source/code artifacts must exist before the dependent task begins.

1. Open the existing commitment and previous review/evidence. Identify which required inputs are present, missing or stale. A folder name does not prove its project is complete.
2. Locate the canonical code/data repository and note its current version. The coordinating files named below live in this weekly folder; implementations and measured outputs belong in the canonical project, linked from evidence.md.
3. Read the plain-language explanation and worked example before selecting sources. Try the small calculation or reasoning exercise on paper. Mark any unfamiliar symbol or word.
4. Use the relevant refresher below inside your allocated study time. Say the idea aloud in your own words, work one example and connect it to a named output. Park material that does not unblock this week.
5. Write the next physical action in TODAY.md and start the allocated block. If a human, dataset or external event is missing, continue independent preparation and mark the dependent task pending.

You have professional experience and are rebuilding dormant foundations; unfamiliar notation is a repairable gap. For each equation, identify the question, read every symbol aloud, attach units, substitute small numbers and only then translate it to code.

## 3. Foundation refreshers you can use without another notebook

### Independent evidence, baselines and leakage

An **observation** is a recorded value; an **independent experimental unit** is the entity whose variation supports your generalization claim. Many timestamps from one batch, fields from one paper, relatives in a protein family or wells from one biological sample are related. Split and analyze at the unit appropriate to the question.

**Training data** fit the model. **Development/validation data** guide choices. **Calibration data** set a threshold or interval under a defined procedure. **Test data** are reserved for the final specified evaluation. These roles may be implemented through carefully designed cross-validation, but their purposes must remain separate. **Leakage** occurs when unavailable information influences a prediction or evaluation—future samples, test-derived scaling, duplicated papers or close sequence relatives can all cause it. [The scikit-learn pitfalls guide](https://scikit-learn.org/stable/common_pitfalls.html) documents preprocessing and leakage traps.

A **baseline** is a simple, relevant reference method. Compare models on the same target, units, observation cutoff and independent groups. Record a split manifest and freeze preprocessing rules before testing. A fixed random seed makes a procedure repeatable; it does not make a bad split valid. When test errors guide a revision, label that test as reused/development and reserve new evidence for the next independent claim. A negative result is still a completed, useful experiment.

### What a number does—and does not—establish

An average summarizes observations but hides spread and sample support. Always ask: what was measured, on how many independent units, with what variability? An **effect size** is a difference or association in a meaningful scale, such as an error reduction in g/L. A small p-value does not make the effect large or useful.

A **confidence interval** comes from a procedure intended to cover a population quantity at a stated rate across repeated sampling under its assumptions. A **credible interval** describes uncertainty under a Bayesian model and its prior. A **prediction interval** concerns a future observation, which is different from uncertainty about an average. A bootstrap resamples data to approximate sampling variability; resample independent groups when rows are related. Report which interval you used and how, rather than writing an unlabeled “±”.

Conformal prediction uses a separate calibration procedure to construct prediction sets/intervals. Its usual guarantees depend on assumptions such as exchangeability of calibration and future cases; shifted faults or new protein families may violate them. Read the assumptions in [Angelopoulos and Bates, sections 1–2](https://arxiv.org/abs/2107.07511) before presenting a guarantee. **Identifiability** asks whether the observed data can distinguish parameter values. An optimizer returning a number is not evidence that the parameter is identifiable. Plot failures and uncertainty, and keep “unknown” available as an answer.

### Sequences, embeddings, assays and biological claims

Proteins are chains built from amino acids. Their sequence influences folding and function in a physical environment. A **homolog** is a related sequence sharing evolutionary ancestry; relatives can make a random test split artificially easy. **Embedding** means a vector produced by a model from the sequence. **Pooling** combines position-level vectors into one sequence representation; **truncation** removes part of a long input. Both change what information reaches the prediction head and must be recorded.

An **assay** is a defined measurement procedure. The label it produces depends on conditions and interpretation. Thermal unfolding, apparent melting temperature, residual enzyme activity and whole-cell performance are different targets. A model trained on one does not automatically predict another. The biological question must specify target, units, assay context and intended population.

An **extremophile** is an organism adapted to an extreme environment; this does not prove every isolated protein is useful under your desired conditions. Computational ranking proposes candidates for review. Experimental validation requires actual measurements and appropriate controls. Biological replicates represent independently prepared biological units; repeated reads of one unit are technical replicates. Keep both identifiers so software does not inflate sample size.

## 4. This week’s specific study bridge

Biology B17: enzyme stability is assay-dependent; compare the actual Meltome/FLIP label methods with the desired industrial question. AI M17: an embedding plus ridge head separates representation generation from supervised fitting. Ridge penalizes large coefficients while predicting the labeled quantity. Start with a bounded subset and freeze protein splits independently of the bioprocess experiment matrix; neither study validates the other.

### Biology/domain assignment retained from the plan

Source unit: B17 · Enzyme stability and engineering

Material: FLIP benchmark paper (Dallago et al. 2021, "FLIP: Benchmark tasks in fitness landscape inference for proteins"); Meltome atlas paper (Jarzab et al. 2020)

Produce: Meltome split loaded; baseline on 1,000 sequences

Understanding check: You know what "thermostability" is measured as

### AI/scientific-method assignment retained from the plan

Source unit: M17 · Deep learning for proteins

Material: MIT 6.874 protein lecture; Hugging Face ESM model card (facebook/esm2_t33_650M_UR50D)

Produce: Embedding + ridge head on Meltome

Understanding check: Baseline number exists

These inherited chapter/lecture lists are resource menus. Select the subsection that answers the named understanding check. Do not add full-course viewing to the 600-minute week. The source/stash section below supplies the existing locators and marks searches as searches.

## 5. Resource stashes: open, study, apply and retain

Open [this week’s resource stash](http://127.0.0.1:5000/learn?stash=LABLOOP-W17%20Notebook) or [the complete weekly deck](http://127.0.0.1:5000/learn?week=17). These addresses work when the local cockpit runs on its default port; use its current address if you changed it. The public source links below also work independently of the cockpit. The weekly stash groups the existing cards; saving a card does not mark it studied.

Use **core** resources first, **run** resources when implementing a specific step, and **deeper** resources only after the critical work fits. Read the stated scope with one question in mind, then produce the note/calculation/artifact before opening another source. Times are inherited estimates, not extra allocations or a promise that every resource fits. Some cards contain an entire course or repository: use only the named topic/exercise.

**Source status.** This catalog preserves the programme’s resources. Direct links are locators, not a claim that all content was freshly inspected. Search links still need an exact source selected. The [source-check log](../../notebooks/source-checks.md) records the targeted checks and corrected FLIP locator; verify mutable versions, access rights, venues, roles and costs when executing. A NotebookLM upload does not automatically import every linked page.

| Card and source | Tier / slot | Scope or task from the existing resource | Time guide |
|---|---|---|---|
| [Dallago et al. (2021), "FLIP: Benchmark tasks in fitness landscape inference for proteins" — search; resolve first](https://scholar.google.com/scholar?q=Dallago+et+al.+%282021%29%2C+%22FLIP%3A+Benchmark+tasks+in+fitness+landscape+inference+for+proteins%22) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-fe5ac4fe) | core / Tuesday | Dallago et al. (2021), "FLIP: Benchmark tasks in fitness landscape inference for proteins", NeurIPS Datasets and Benchmarks (bioRxiv 2021.11.09.467890) — introduction, task table, thermostability split (40 min) | 40 min |
| [Jarzab et al. (2020), "Meltome atlas — thermal proteome stability across the tree of life" — search; resolve first](https://scholar.google.com/scholar?q=Jarzab+et+al.+%282020%29%2C+%22Meltome+atlas+%E2%80%94+thermal+proteome+stability+across+the+tree+of+life%22) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-8cf9f0e5) | core / Tuesday | Jarzab et al. (2020), "Meltome atlas — thermal proteome stability across the tree of life", Nature Methods 17:495-503 — abstract and figure 1 (20 min) | 20 min |
| [Hugging Face model card facebook/esm2_t33_650M_UR50D — search; resolve first](https://www.youtube.com/results?search_query=Hugging+Face+model+card+facebook%2Fesm2_t33_650M_UR50D) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-9792d3db) | core / Thursday | Hugging Face model card facebook/esm2_t33_650M_UR50D (10 min) + MIT 6.874 protein lecture second half (40 min); code block with ridge head | 40 min |
| [Yang, Wu & Arnold (2019), "Machine-learning-guided directed evolution for protein engineering" — search; resolve first](https://www.youtube.com/results?search_query=Yang%2C+Wu+%26+Arnold+%282019%29%2C+%22Machine-learning-guided+directed+evolution+for+protein+engineering%22) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-9e32cd79) | deeper / Saturday · sandbox, optional | Yang, Wu & Arnold (2019), "Machine-learning-guided directed evolution for protein engineering", Nature Methods 16:687-694. The classic review; read it before week 21 | 15 min (estimate) |
| [Frances Arnold, Nobel Lecture 2018 "Innovation by Evolution: Bringing New Chemistry to Life" — source](https://nobelprize.org) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-04f83561) | deeper / Saturday · sandbox, optional | Frances Arnold, Nobel Lecture 2018 "Innovation by Evolution: Bringing New Chemistry to Life" (nobelprize.org) | 15 min (estimate) |
| [Notin et al. (2023), "ProteinGym: large-scale benchmarks for protein fitness prediction and desi… — search; resolve first](https://scholar.google.com/scholar?q=Notin+et+al.+%282023%29%2C+%22ProteinGym%3A+large-scale+benchmarks+for+protein+fitness+prediction+and+desi%E2%80%A6) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-8f4f6a72) | deeper / Saturday · sandbox, optional | Notin et al. (2023), "ProteinGym: large-scale benchmarks for protein fitness prediction and design", NeurIPS Datasets and Benchmarks | 30 min (estimate) |
| [Andrew White, Deep Learning for Molecules and Materials — dmol.pub, chapter on "Deep learning on… — search; resolve first](https://github.com/search?q=Andrew+White%2C+Deep+Learning+for+Molecules+and+Materials+%E2%80%94+dmol.pub%2C+chapter+on+%22Deep+learning+on%E2%80%A6&type=repositories) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-71181387) | deeper / Saturday · sandbox, optional | Andrew White, Deep Learning for Molecules and Materials — dmol.pub, chapter on "Deep learning on sequences" (with Colab buttons) | 30 min (estimate) |
| [StatQuest "Ridge vs Lasso Regression, Visualized!!!" — search; resolve first](https://www.youtube.com/results?search_query=StatQuest+%22Ridge+vs+Lasso+Regression%2C+Visualized%21%21%21%22) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-0cc6ea6a) | core / Any block this week — the plan names no day | StatQuest "Ridge vs Lasso Regression, Visualized!!!" (YouTube) for the head you fit | 15 min (estimate) |
| [J-SNACKKB/FLIP — source](https://github.com/J-SNACKKB/FLIP) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-46e0666b) | run / Any block this week — the plan names no day | Download the meltome splits; read the README on "mixed", "human" and "human_cell" splits; load them with pandas | 30 min |
| [OATML-Markslab/ProteinGym — source](https://github.com/OATML-Markslab/ProteinGym) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-ea49a18a) | run / Any block this week — the plan names no day | Browse the leaderboard page and the baselines list; note where ESM-2 sits | 30 min |
| [huggingface/notebooks — source](https://github.com/huggingface/notebooks) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-cb219802) | run / Any block this week — the plan names no day | Reuse protein_language_modeling.ipynb to cache mean-pooled embeddings to disk | 30 min |
| [yangkky/Machine-learning-for-proteins — source](https://github.com/yangkky/Machine-learning-for-proteins) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-a7ddf2bc) | run / Any block this week — the plan names no day | The curated reading list by Kevin Yang; pick 2 papers for PARKING.md, not more | 30 min |
| [scikit-learn/scikit-learn — source](https://github.com/scikit-learn/scikit-learn) · [card](http://127.0.0.1:5000/learn?week=17#card-w17-cdcbca1b) | run / Any block this week — the plan names no day | RidgeCV on embeddings → melting temperature; report Spearman correlation with a bootstrap interval | 30 min |

### A bounded way to read a scientist’s paper

Use the founder-paper slot only where scheduled. Select a paper relevant to this week through the existing resources or a verified author profile; record title, authors, year, exact URL and what full text you could access. In 30 minutes: 5 for the question and experimental unit; 10 for one relevant methods passage and figure; 10 for a digest; 5 for three questions. Your digest should say question → method → measured result with N/units → limitation → connection to this week’s artifact. If you only read the abstract, say so and leave inaccessible methods/results unknown. This notebook has not read a future partner paper on your behalf.

## 6. Seven-day runbook: what to do, why and when to stop

Use the original weekly allocation: six 90-minute days plus a 60-minute Sunday, totaling 600 minutes. Reading this notebook, creating study visuals, debugging, calls and evidence recording all consume that same capacity. This is a reference you open section by section, not an additional course to finish before building.

| Day | Budget | Existing target and stopping condition |
|---|---:|---|
| Monday | 90 min | Run mechanistic/pure ML/hybrid by 5/10/20/50 training batches by five seeds with separate training, calibration and test groups. Stop when: All 60 planned model-size-seed combinations have results or explicit failure reasons. |
| Tuesday | 90 min | FLIP benchmark paper (Dallago et al. 2021, "FLIP: Benchmark tasks in fitness landscape inference for proteins"); Meltome atlas paper (Jarzab et al. 2020) Produce: Meltome split loaded; baseline on 1,000 sequences |
| Wednesday | 90 min | Evaluate faulty-batch transfer and monitoring timing; report prediction errors, interval coverage/width, false alarms and misses. Stop when: Coverage on shifted faults is measured, not guaranteed; test information does not tune models. Reserve the last 10 minutes for the optional Wednesday note. |
| Thursday | 90 min | MIT 6.874 protein lecture; Hugging Face ESM model card (facebook/esm2_t33_650M_UR50D) Select material/exercise that fits 60 minutes; use the last 30 for the founder paper. |
| Friday | 90 min | Freeze configs, splits and figures; package batchlens v0.3 and Z4 materials in the canonical Paper 2 repository. Stop when: A bounded reproduction command and full-run command are documented with resource estimates. |
| Saturday | 90 min | Resolve the earliest critical spillover or dependency. Prepare any long-form publication draft from existing figures here if needed. Sandbox only after commitments are met. |
| Sunday | 60 min | 20 min build-log draft/publication when authorized; 5 min evidence/checklist; 20 min source-grounded gap review; 15 min next week's six physical actions. |

### Monday challenge — `paper-2-results.csv`

**Do these steps in order.**

1. Confirm the Week 12 matrix, actual available batch counts and frozen split files.
2. Execute each of the 60 model-size-seed combinations or save a specific failure reason.
3. Record config, data/code versions, prediction outputs, runtime and model status.
4. Use the same test groups for paired comparisons and avoid tuning on their results.
5. A model that fails to converge belongs in the report.

**Why this matters.** This output closes the input/question part of the week’s evidence chain. Its acceptance criterion below determines whether the result can support the next milestone.

**Output and acceptance from the specification:** Run mechanistic/pure ML/hybrid by 5/10/20/50 training batches by five seeds with separate training, calibration and test groups. Acceptance: All 60 planned model-size-seed combinations have results or explicit failure reasons.

**Block allocation:** 2 minutes open the current inputs and write the immediate action; 78 minutes carry out the steps above; 7 minutes check the acceptance criterion and record output paths/errors; 3 minutes write the next action. These sum to 90 minutes. Stop at the limit and record the exact next step rather than silently expanding the week.

**Evidence to retain:** the artifact or pending-state runbook, input/output paths, version/configuration, the check performed, observed result or exact failure, and your interpretation. Put the link in evidence.md. An intended filename is not proof that a measured output exists.


### Wednesday challenge — `paper-2-transfer.csv`

**Do these steps in order.**

1. Evaluate normal and faulty held-out groups separately.
2. Compute prediction errors, calibrated interval coverage and width, monitoring false alarms, misses and timing under the frozen rules.
3. Show how many independent batches support each estimate.
4. Inspect fault cases as distribution shift; do not claim conformal guarantees automatically survive it.
5. Preserve per-batch results so aggregation can be audited.

**Why this matters.** This output closes the implementation/measurement part of the week’s evidence chain. Its acceptance criterion below determines whether the result can support the next milestone.

**Output and acceptance from the specification:** Evaluate faulty-batch transfer and monitoring timing; report prediction errors, interval coverage/width, false alarms and misses. Acceptance: Coverage on shifted faults is measured, not guaranteed; test information does not tune models.

**Block allocation:** 2 minutes open the current inputs and write the immediate action; 68 minutes carry out the steps above; 7 minutes check the acceptance criterion and record output paths/errors; 3 minutes write the next action. The remaining 10 minutes are the optional Wednesday note; reclaim them for the critical task if necessary. These sum to 90 minutes. Stop at the limit and record the exact next step rather than silently expanding the week.

**Evidence to retain:** the artifact or pending-state runbook, input/output paths, version/configuration, the check performed, observed result or exact failure, and your interpretation. Put the link in evidence.md. An intended filename is not proof that a measured output exists.


### Friday challenge — `paper-2-reproducibility.md`

**Do these steps in order.**

1. Freeze the results table and generate learning curves and paired comparison plots.
2. Package a bounded smoke reproduction command plus the full-run command, expected resources and all failure states.
3. Prepare batchlens v0.3 and Paper 2 artifact links.
4. Run only the allocated protein baseline exercise, keeping its dataset, target and outputs in a separate study path.

**Why this matters.** This output closes the verification/handoff part of the week’s evidence chain. Its acceptance criterion below determines whether the result can support the next milestone.

**Output and acceptance from the specification:** Freeze configs, splits and figures; package batchlens v0.3 and Z4 materials in the canonical Paper 2 repository. Acceptance: A bounded reproduction command and full-run command are documented with resource estimates.

**Block allocation:** 2 minutes open the current inputs and write the immediate action; 78 minutes carry out the steps above; 7 minutes check the acceptance criterion and record output paths/errors; 3 minutes write the next action. These sum to 90 minutes. Stop at the limit and record the exact next step rather than silently expanding the week.

**Evidence to retain:** the artifact or pending-state runbook, input/output paths, version/configuration, the check performed, observed result or exact failure, and your interpretation. Put the link in evidence.md. An intended filename is not proof that a measured output exists.


### Tuesday — rebuild the domain concept through an output

1. Spend 10 minutes recalling what you know and listing the specific confusing words in the domain assignment above.
2. Spend up to 35 minutes on the relevant source subsection. Pause to define each term using the actual data or artifact, not only an analogy.
3. Spend 30 minutes producing the assigned note, drawing, calculation or bounded exercise. Label invented teaching numbers as synthetic. For applied weeks, use the exercise/decision in the current challenge.
4. Spend 10 minutes explaining what the concept changes in this week’s work. Record the explanation or write five sentences without looking.
5. Spend 5 minutes saving the output and one unresolved question. These 90 minutes include opening/closing rituals. For a source week with no new coursework, apply this rhythm to its named operational material.

### Thursday — connect the model or decision to the domain

Use 5 minutes to recall the concept, 20 for the smallest relevant source section, 25 for one calculation/code application, 5 to explain it without notes and 5 to log the result. Use the remaining 30 for the scheduled founder-paper digest. If the inherited list is longer than the block, park the rest.

Write one bridge sentence: “I use [concept] in [specific artifact] because it prevents or enables [specific behavior].” Then point to the corresponding field, equation, comparison or paragraph. If you cannot locate the connection, return to the smallest worked example rather than opening more resources.

### Saturday — repair the earliest dependency, then prepare study visuals

Start by reading unresolved acceptance items and choose the one that blocks the next milestone. Spend 2 minutes choosing it, 58 minutes fixing or preparing the smallest checkable result, 7 minutes checking the result and 3 minutes recording the handoff. If critical work is complete, use the remaining 20 minutes to import this notebook or prepare the next week’s sources and request one visual. If not, use those 20 minutes on the dependency. Optional sources and polish move to PARKING.md. A visual study aid does not replace the missing artifact.

### Sunday — review and make next week startable

Use 20 minutes to draft a build log from one actual figure, result or failure, with correct publication state; 5 minutes to update evidence and checklist; 20 minutes to attempt the recall questions and review gaps; 15 minutes to write six next physical actions with filenames and needed inputs. Keep the original gate target and record any revised forecast separately. Actual external publishing follows the authorization available in that execution session.

## 7. Worked example, practice and separate answer key

### Worked teaching example

Synthetic paired errors for three test batches: mechanism [2,4,3], hybrid [1,5,2] g/L. Hybrid-minus-mechanism is [-1,+1,-1], average -0.33 g/L. The hybrid improves on two batches but worsens on one. Three batches provide little support for a broad superiority claim.

These are illustrative reasoning examples or explicitly synthetic numbers, not measurements obtained for your research. Recreate the calculation or reasoning before applying it to actual inputs.

### Your exercise — answer before reading the solution

Does five seeds evaluated on the same ten test batches give 50 independent test batches?

Your answer: ____________________

Your connection to `paper-2-results.csv`: ____________________

### Answer key

No. The same ten process runs are reused. Report both seed variation and independent-batch N, and choose an uncertainty analysis that respects the repeated paired structure rather than pretending every score is independent.

### Five recall questions and grading guide

1. Explain this week’s purpose to a 15-year-old in three sentences. What real scientific or professional decision does it support?
2. Solve the exercise above and explain each assumption or unit, rather than copying the answer.
3. Open `paper-2-results.csv` or its preparation instructions. What exact input, action, output and acceptance check make it inspectable?
4. What could make `paper-2-transfer.csv` look convincing while still being wrong? Use the week-specific risk below and name the evidence needed to detect it.
5. What does `paper-2-reproducibility.md` pass to the next milestone, and which claim remains unsupported until external or measured evidence exists?

Grade each answer 0 (missing/wrong), 1 (correct but vague or needs prompting), or 2 (correct, explained in your own words and tied to a concrete artifact/example). The purpose/bridge supports Q1; the separate key supports Q2; the runbook and contracts support Q3–Q5. Aim for at least 8/10 with no unresolved misconception about provenance, independence, measured versus predicted results or external status. This is a study-readiness check, not permission to pass the research gate. Save your actual score and gaps; this prepared notebook does not assign them.

## 8. Research evidence and professional profile

### What your weekly evidence record should contain

Copy the blank structure below into the existing evidence.md only when working the task. Keep earlier entries. Use actual values or PENDING with a reason; never copy illustrative numbers as results.

| Evidence field | Your actual entry |
|---|---|
| Question/decision and why it matters | PENDING |
| Artifact name, canonical path and version | PENDING |
| Input origin, access date, rights, version/checksum | PENDING |
| Unit of analysis / target / relevant units | PENDING |
| Method, baseline, assumptions and split or decision rule | PENDING |
| Command/config/seed or document review procedure | PENDING |
| Observed output, independent N and uncertainty where relevant | PENDING |
| Check performed, failures and negative findings | PENDING |
| Interpretation and what the evidence does not establish | PENDING |
| Your contribution and model/human/partner contributions | PENDING |
| External event state and dated receipt/link, if applicable | PENDING |
| Next action, owner and dependency | PENDING |

For documentary or commercial work, replace nonapplicable experimental fields with the actual verification (for example, source-to-claim audit or agreed scope/receipt); write “not applicable” with a reason instead of inventing a metric. For experiments, retain raw data, scripts/configs, per-case results and generated figures in the canonical project. Evidence.md is the index, not a substitute for those artifacts.

### Make the skill-to-proof connection

Complete this sentence from real work: “I used [systems/software/AI capability] to answer [scientific/workflow question], implemented [artifact], checked it using [method], observed [result with context], and found [limitation]. My contribution was [specific work].” This becomes an honest portfolio/interview story. A failed experiment with a sound diagnosis is also evidence of research judgment.

The Week 40 profile should let a reader follow your claims to data/code/releases, manuscripts with exact status, independent feedback, collaboration and actual delivery. Keep a brief record now so you do not reconstruct a research history from memory later.

## 9. Completion checklist, misconceptions and recovery

This is an enabling week. Its deliverables feed the next scheduled gate: 20. Complete the acceptance checklist below; do not promote reputation from a draft.

- [ ] Monday artifact satisfies: All 60 planned model-size-seed combinations have results or explicit failure reasons.
- [ ] Wednesday artifact satisfies: Coverage on shifted faults is measured, not guaranteed; test information does not tune models.
- [ ] Friday artifact satisfies: A bounded reproduction command and full-run command are documented with resource estimates.
- [ ] Study pack is prepared; Peter's actual learning/quiz result is recorded separately.
- [ ] Branding and opportunity drafts use supported claims and the correct external-action state.
- [ ] Reproduction or document checks are recorded with file/version references.
- [ ] This week's special gate, if any, has real evidence or remains explicitly pending.
- [ ] Review, root progress/log and the next physical action are updated.

### Notebook study checklist

- [ ] I can describe the week’s question and its connection to my systems/software/AI work.
- [ ] I repaired the specific prerequisite I needed and completed the worked exercise myself.
- [ ] I used the selected stash sources and saved an applied note rather than only a viewing history.
- [ ] Each challenge has the specified output and a recorded acceptance check, or an explicit missing dependency.
- [ ] I retained source versions, units, split/decision rules, failures and limitations where applicable.
- [ ] I can distinguish illustrative, simulated, predicted and measured information in my work.
- [ ] My research/profile claims link to real evidence and use the correct external status.
- [ ] I recorded actual recall performance, next actions and any unmet gate requirement.

### The specific trap to avoid this week

Meltome targets and engineered-enzyme thermostability may differ. Keep early protein baselines separate from Paper 2 and avoid selecting results based on hoped-for wins.

Apply the root scientific evidence contract: provenance; meaningful independent splits; no future-data leakage; baseline comparison; actual denominators/uncertainty; failed cases; human review where required. State whether data are simulated, public experimental, synthetic or partner-provided.

Keep source gates intact, and log scope/time conflicts in review. Missing people, money, access or results must not be replaced with invented evidence. Reduce optional polish first. A specific essential external dependency stays pending while independent preparation continues.

### Recovery without losing the programme’s meaning

Source-preserving minimum: the learning curves with seeds.

Operational first step if stalled: open `paper-2-results.csv`, write the required inputs and produce the smallest checkable example in ten minutes. If a more urgent missing gate dependency controls the week, use the source minimum above.

If the full week does not fit, use [P14](../../../LABLOOP-Master-Plan-v6.md#part-8--the-prompt-pack-copy-paste-fill-the-brackets): retain one gate-critical deliverable and one directly supporting study exercise; record what is skipped versus moved. Do not mark the original full gate passed merely because the minimum was completed. After a missed week, reforecast from actual evidence and avoid a second unexamined lost week.

If a prerequisite concept is missing, use the first ten minutes to define it and work one tiny example. If hardware or access fails, use the allowed offline/synthetic preparation and label its scope. If a person, public identifier or payment is required, keep that event pending and finish independent work. A smaller preparation result preserves momentum; it does not replace an unmet gate.

## 10. NotebookLM import and visual study kit

1. Create a notebook named **LABLOOP-W17 · paper 2 complete experiments**.
2. Upload this week’s `notebooklm.txt` or `learning-notebook.md`, or paste the complete text as a source. Upload one copy, not both. [Google’s source-import guidance](https://support.google.com/notebooklm/answer/16215270?hl=en) documents text/Markdown and pasted-text sources.
3. Add the exact core source pages or permitted PDFs you selected in Section 5 as separate sources. Linked pages and local cockpit stashes are not recursively imported. Inspect what was actually imported; a video may require an available transcript. You do not need all 454 programme cards in one notebook.
4. When you execute the week, add your own notes, permitted code excerpts, results and review as separate clearly named sources. Use “Measured — …” only for actual measurements. Keep sensitive partner/client records within their permitted use.
5. Request one study output at a time using the prompts below; use the available Studio feature or ask chat for the equivalent outline. Interface/features can vary. Inspect units, labels, citations and the separation between planned and achieved work before studying or sharing a generated visual.

### Week-specific visual brief

Draw the 3×4×5 experiment grid, include failed cells, and connect it to learning curves with batch-aware uncertainty.

### Copy-ready prompts

**Concept map / infographic**

> Using only the sources in LABLOOP-W17, explain “paper 2 complete experiments” to an adult software/systems/AI professional rebuilding science foundations after twenty years. Draw the 3×4×5 experiment grid, include failed cells, and connect it to learning curves with batch-aware uncertainty. Label each arrow with why it exists. Define every technical term at first use. Connect prerequisite → current challenge → evidence → next milestone. Mark targets, synthetic teaching numbers and measured results distinctly. Cite sources; identify missing evidence instead of inventing it.

**Visual lesson / slides**

> Create an eight-panel visual lesson for Week 17: 1 purpose, 2 needed foundations, 3 one worked example with units, 4 Monday challenge, 5 Wednesday challenge, 6 Friday challenge, 7 common failure and verification, 8 evidence checklist and next milestone. Use simple explanations suitable for a 15-year-old without talking down to an experienced professional. Put detailed speaker notes under each panel. Use the exact artifact filenames and acceptance criteria in this notebook.

**Audio or video study script**

> Teach Week 17 as a guided conversation: what, why, when and how. Explain the mathematical/scientific idea in words before notation. Pause for me to answer the exercise without revealing the solution; then walk through it. Connect my software experience to the scientific question. End with a rehearsal of what evidence I must retain, not a claim that I have already completed the work.

**Flashcards and quiz**

> Make twelve cards: four term/meaning/example cards, four how/why steps from the actual challenges, two unit/assumption checks and two evidence/misconception checks. Then ask five open questions one at a time. Grade each 0–2 using the notebook rubric, explain any error, and cite the relevant section. Do not infer my competence from the existence of the notebook.

**Research review rehearsal**

> Act as a careful research reviewer for Week 17. Ask me to defend the question, inputs, independent unit or decision basis, baseline, method, uncertainty, failure cases and scope of the claim. Use only supplied evidence. Distinguish a preparation artifact from a measured result and an external event. Finish with a claim-to-evidence table and the three smallest actions needed to close real gaps.

## 11. Source and ownership notes

This notebook adds explanation and runbooks to the existing plan. The [weekly specification](./specs.md), [programme decisions](../../DECISIONS.md) and [gates](../../GATES.md) retain authority over sequence, scope and acceptance. The [v6 master](../../../LABLOOP-Master-Plan-v6.md) supplies the dependency map and inherited resource cards; the [source catalog](../../SOURCES.md) records their history. The teaching examples and explanatory bridge are preparation material, not executed findings.

Existing files and learning progress remain separate. Dates stay relative until you choose actual commitments. Peter owns learning, personal claims, scientific responsibility and external relationships; independent humans own their actual annotation/review contributions and partner laboratories own their laboratory execution. This notebook prepares the steps; it does not publish, contact, spend, submit or perform the programme on your behalf.