LABLOOP W03 — statistical batch monitoring: full learning notebook

You will turn an exploratory PCA plot into a repeatable monitoring rule. An alarm needs a definition of normal, a numerical score, a threshold and a decision about persistence. Hotelling T-squared asks whether a sample is unusually far along the learned PCA directions. SPE, squared prediction error, asks whether it lies far outside the space those directions represent. Both can be useful; neither knows the biological cause of an abnormal measurement.

Your systems, software and AI connection. Think of this as observability plus an alert policy. You already know that a noisy alert system can become unusable. The research addition is separating threshold calibration from evaluation and measuring missed faults as carefully as successful detections. These choices determine the credibility of the Week 04 release.

Dependency map from the existing programme.

• ← From: week 2 PCA on normal batches; week 1 injected fault.
• → Feeds: week 4 (hours-to-detection and false-alarm rate), week 8 (technical report "golden batch" section), week 17 (hours-to-detection per model in Paper 2), week 33-35 (batchlens alarms trigger labdocs deviation drafts).
• ⟳ Returns in: every Loop Audit (week 29): "how early would you have known?"

Gate and proof required by the current weekly specification.

This is an enabling week. Its deliverables feed the next scheduled gate: 4. 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.

• Week 02: 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.

Vectors, matrices and PCA as operations on measurements

A scalar is one number. A vector is an ordered list: [biomass, sugar, product] at one time. A matrix is a rectangular table of numbers, often observations in rows and measurements in columns. Shape matters: a 100×5 matrix contains 100 observation rows and five features; it does not automatically mean 100 independent batches. Write the column names and units beside the matrix.

Multiplying a matrix by a vector forms weighted combinations. For A=[[1,2],[0,1]] and v=[3,4], Av=[1×3+2×4, 0×3+1×4]=[11,4]. This same operation appears in regression, neural networks and dimension reduction. A fitted model learns some of those weights from data.

PCA, principal component analysis, finds directions of variation in centered data. Think of rotating your view of a cloud of points to see its longest dimension. The coordinates in the new directions are scores; the directions’ weights are loadings. Reconstructing from a few components leaves a residual. Variables with different scales can dominate unless scaling is justified, so fit scaling on training data and record it. A component with large variance is not automatically biologically important or predictive of your target. Practice by drawing two correlated sensor channels and their long axis.

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 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 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.

Biology B3: lag is adaptation, log phase is approximately exponential growth under suitable conditions, stationary phase reflects limiting conditions, and decline is loss of viable cells. A simplified Monod model need not represent all four. AI M3: bias is systematic mismatch; variance is sensitivity to the training sample. Regression predicts a numeric target, with residual=observed−predicted. Fit a small linear baseline and inspect residuals rather than assuming a more flexible model is better.

Biology/domain assignment retained from the plan

Source unit: B3 · Microbial growth

Material: OpenStax Microbiology ch. 9 "Microbial Growth" — openstax.org/details/books/microbiology

Produce: Notes + the growth curve drawn by hand with lag, log, stationary, death

Understanding check: You can connect the log phase to the Monod equation in your simulator

AI/scientific-method assignment retained from the plan

Source unit: M3 · Statistical learning I

Material: An Introduction to Statistical Learning with Python (ISLP) ch. 2 and 3 — statlearning.com (free PDF + lecture videos)

Produce: Lab 3 redone on IndPenSim variables

Understanding check: You know bias-variance and can fit and read a regression

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.

Open this week’s resource stash or the complete weekly deck. 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 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.

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.

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.

Monday challenge — monitoring-protocol.md

Do these steps in order.

1. Freeze the input representation and independent splits from Week 02.
2. For each standardized measurement, calculate PCA scores and reconstruction residuals.
3. Define T2 as the sum of squared component scores divided by their training variances, and SPE as the sum of squared reconstruction residuals.
4. Inspect near-zero variances and missing channels.
5. Choose a documented threshold estimator using training/calibration normal batches.
6. State the intended alarm rate and whether it is per sample or per batch.

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: Estimate PCA Hotelling T2 and SPE control limits using only training/calibration normal batches; choose alarm persistence before evaluating faults. Acceptance: Limit estimator, sample support and nominal alarm policy are reproducible.

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 — alarm-examples.csv

Do these steps in order.

1. Write the alarm rule before replaying faults, for example three consecutive threshold exceedances.
2. Replay measurements in timestamp order using only available information.
3. Save raw scores, threshold, consecutive count, alarm time and known fault onset.
4. Preselect normal and fault examples using metadata.
5. Retain no-alarm cases explicitly; never replace an undetected event with a zero delay.
6. Review normal alarms to see whether missingness or phase changes explain them.

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: Implement batch replay with timestamped alarms and compare normal/fault cases selected independently of results. Acceptance: Event timing and undetected cases are retained; no cherry-picked pass rule.

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 — verification-report.md

Do these steps in order.

1. Run a small offline fixture with known behavior: an ordinary sequence, an extreme point, a missing channel and a sustained excursion.
2. Confirm the persistence counter resets correctly, training transforms remain frozen and the report uses the intended batch labels.
3. Record commands/configuration and one normal plus one fault figure.
4. Link the verification report to the exact model objects and raw score table so another person can inspect the alert 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: Check leakage, missing channels and fake-data reproducibility; prepare a one-command monitoring report. Acceptance: The smoke run works without network or credentials and numerical invariants are checked.

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.

Worked teaching example

Synthetic score trace: threshold=5; scores are 4, 6, 7, 3, 6, 7, 8 at minutes 0–6. With three consecutive exceedances the alarm occurs at minute 6. The earlier two high values do not trigger it because the score 3 resets the count. The rule trades faster detection for fewer isolated alerts.

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

If each sample has a small false-alarm probability, does that guarantee a whole long batch rarely alarms? Why must persistence be frozen first?

Your answer: ________

Your connection to monitoring-protocol.md: ________

Answer key

No. A batch contains many opportunities to alarm, with dependence between samples. Measure the batch-level false-alarm fraction directly. Tuning persistence after inspecting test faults uses their outcomes to choose the policy and invalidates an untouched test claim.

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 monitoring-protocol.md or its preparation instructions. What exact input, action, output and acceptance check make it inspectable?
4. What could make alarm-examples.csv look convincing while still being wrong? Use the week-specific risk below and name the evidence needed to detect it.
5. What does verification-report.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.

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.

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.

This is an enabling week. Its deliverables feed the next scheduled gate: 4. Complete the acceptance checklist below; do not promote reputation from a draft.

• Monday artifact satisfies: Limit estimator, sample support and nominal alarm policy are reproducible.
• Wednesday artifact satisfies: Event timing and undetected cases are retained; no cherry-picked pass rule.
• Friday artifact satisfies: The smoke run works without network or credentials and numerical invariants are checked.
• 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

Unfolding a whole batch can leak the future into early warning. Require causal feature availability for every alarm timestamp.

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: T² limits computed.

Operational first step if stalled: open monitoring-protocol.md, 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: 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.

1. Create a notebook named LABLOOP-W03 · statistical batch monitoring.
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 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 a point inside/outside the PCA plane, then a score timeline with threshold, persistence counter, fault onset and first alarm.

Copy-ready prompts

Concept map / infographic

Using only the sources in LABLOOP-W03, explain “statistical batch monitoring” to an adult software/systems/AI professional rebuilding science foundations after twenty years. Draw a point inside/outside the PCA plane, then a score timeline with threshold, persistence counter, fault onset and first alarm. 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 03: 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 03 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 03. 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.

This notebook adds explanation and runbooks to the existing plan. The weekly specification, programme decisions and gates retain authority over sequence, scope and acceptance. The v6 master supplies the dependency map and inherited resource cards; the source catalog 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.