Week 01 — fermentation simulator

This week delivers the three concrete artifacts below and the supporting learning/branding/opportunity pack. The scientific or commercial outcome follows the preserved source brief, while the acceptance checks make its evidence testable. The career objective is to turn this work into something a scientist, hiring manager or buyer can inspect.

• Scientific/build priority: Implement X/S/P/DO balances, feed and volume terms, named parameters and units; document the scope of the simplified simulator.
• Public/profile priority: Draft one simulation plot post and a brief explanation of the measurement-to-decision loop.
• Career/relationship/commercial priority: Digest one verified Michroma or Levya author paper; save a question about measurement workflows and one potential feedback contact.

Read v6 · Week 1 · A tank in 80 lines before Monday. It carries this week's v5 brief unchanged and adds the Welcome analogy, Learn it (▶ core and ＋ deeper sources), Run it (repos with a 20-40 minute exercise), the NotebookLM kit and the mentor check below. Standing v6 rules: resource rules, NotebookLM protocol, chapter anatomy. v6 adds depth only; it does not change a goal, gate, output or the 600-minute cap, so this specification still governs execution.

Verified links, exact scopes and durations for this week's reading and watching: materials.md.

Act I · Threads: B, M, BL · Next gate: 4

Where this week sits. - ← From: week 0 cockpit and batchlens repo. - → Feeds: week 2 (you will recognise X, S, P and DO in 100 real batches), week 3 (your fake fault becomes the first thing your alarms must catch), week 9-11 (the same equations reappear as mass balances in NPTEL), week 12 (your Monod model becomes the "mechanistic half" of the hybrid model), week 17 (Paper 2 learning curves). - ⟳ Returns in: week 12 and Paper 2. The 80 lines you write this week are literally the physics inside Paper 2's hybrid model.

Goals. - 🎯 This week: a simulator that shows lag, log and stationary phases, streamed live, plus 20 seeded batches with one labelled fault. - 🏁 Next milestone: Gate 4 (batchlens v0.1 with DOI). - 🌟 Final: Paper 2 and JOSS: a hybrid soft sensor whose mechanistic core is the model you start today.

Mentor check. "I can draw the growth curve, point at each phase, and show the line of code that creates it."

• Week 00: inspect its actual evidence and review; source/code artifacts must exist before the dependent task begins.
• Read commitment, evidence, review and the current root TODAY/PROGRESS/QUESTIONS files.
• Read this week's chapter, v6 · Week 1 · A tank in 80 lines, then the standing sections: Part 0 · how to use the document, Part 3 · the scientific thesis, Part 4 · curriculum, Part 8 · the prompt pack and Part 9 · branding and endorsement. The same sections exist in the v5 master, which remains the governing sequence; v6 reproduces them and adds depth. Retained commercial/instrument details come from v4.
• Inspect the actual canonical repository state, available data, existing figures and required human/external inputs. Never assume a previous week succeeded from its folder name.
• Verify current resource links, versions, dataset rights and any live company/venue information before relying on them. Use source catalog; explicitly record inaccessible resources.

The model must turn these into exact physical actions using the observed files and available inputs. Each 90-minute block includes start/stop rituals. All calls, posts, debugging, reading and exercises consume this same capacity.

Each day is one mission: the Hook gets you in, Done when tells you to stop. Study stops at the understanding check. If a piece feels pointless, ask whether Done when needs it: no → skip and log; yes → let AI do the boring part and review it; still stuck → smallest version in 25 minutes.

Biology/domain unit

Source unit: B1 · Molecules of life and enzymes

Material: OpenStax Biology 2e 6.5 Enzymes only (trimmed 2026-09-29; ch. 3 and Crash Course are Deeper)

Produce: five lines in notes/bio/B1.md

Understanding check: You can say what a protein, an enzyme and a substrate are without looking

During preparation, write one worked example connected to the week's actual task, one exercise with a separate answer key and five questions. Peter supplies answers; score explanation quality and flag misconceptions in QUESTIONS. Numeric/scientific conclusions require actual data or explicitly synthetic teaching values.

AI/scientific-method unit

Source unit: M1 · Linear algebra, intuitively

Material: 3Blue1Brown "Essence of Linear Algebra" ch. 1-3 (trimmed 2026-09-29; ch. 4-7 are Deeper)

Produce: one example redone in NumPy

Understanding check: You can say what a matrix does to a vector

Use the prompt pack: P1 explanation ladder, P2 concept-to-project bridge and P3 quiz. Coding practice belongs within the allocated study/build block. Where the source asks for a favorable scientific result, substitute an honest measured comparison as explained below.

Founder/scientist paper

Select one verified founder/scientific-lead paper relevant to this week's domain. Use P11; save the question, method, numerical result with context, data workflow, three author questions and one connection sentence in founder-paper.md. Keep abstract-only access limitations explicit. On source weeks with no study, prioritize the commercial/submission task and log any skipped digest.

These filenames are outputs to prepare during this week's execution; this scaffold does not claim they already exist. Drafts and measured results have different states. Technical code/data stay in the canonical project repository and are referenced from these coordinating files.

During preparation, future measured tables get column definitions, input requirements and run instructions with PENDING status. During execution, replace pending values only with measured outputs. Do not create realistic-looking fake evidence.

Branding: Draft one simulation plot post and a brief explanation of the measurement-to-decision loop.

Opportunity action: Digest one verified Michroma or Levya author paper; save a question about measurement workflows and one potential feedback contact.

Use career asset contracts. Publish at most the agreed cadence; one figure and one supported number or admitted failure can support multiple drafts. In long-form weeks, prepare a 1,500–2,500-word article from existing evidence within the build/buffer allocation. In release/demo weeks, prepare a 60-second script: problem, input, command/action, result, limitation and proof link.

Verify individuals, jobs and affiliations at execution time. The GridX list and v4 company map are research seeds, not current facts. Do not reveal named company weaknesses or employer/client confidential information. Keep drafted/sent/replied/booked/held statuses separate. The planning request alone does not authorize external messages or publication; honor explicit authorization in the execution session.

A basic Monod model does not automatically produce lag and death phases. Eighty lines is a presentation idea, not a correctness criterion; do not force artificial physiology to match the title.

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.

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: A repeatable trajectory and balance checks exist; any lag-phase behavior is explicitly modeled.
• Wednesday artifact satisfies: A timestamped sample can be traced from generator to rendered curve.
• Friday artifact satisfies: Twenty reproducible outputs have known ground truth and no implied plant validation.
• 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.

Source-preserving minimum: monod.py with the plot.

Operational first step if stalled: open simulator-design.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.

In review, state exactly what exists, what was verified, what Peter can now explain, which external events remain pending, where the canonical code/data live, and the next file/function or draft paragraph to touch. Link each fact to evidence. Carry scientific assumptions, split definitions and rights constraints forward explicitly.

Prepare Week 01 of this LABLOOP execution plan.
Read execution-plan/specs.md, execution-plan/DECISIONS.md,
execution-plan/GATES.md and
execution-plan/weeks/week-01-fermentation-simulator/specs.md, commitment.md, evidence.md and review.md.
Read prior prerequisite reviews and inspect actual repository/data state.
Use v5 as the governing sequence, the v6 Week 01 chapter
(LABLOOP-Master-Plan-v6.md) as the deeper walkthrough, and v4 only for
retained details.
Produce the full local preparation pack and the exact week-specific files named
in the artifact table. Give me a bounded lesson with exercises/answer key,
daily physical actions, technical runbook or draft, branding drafts and
career/commercial actions. Keep the total to 600 minutes.
Separate prepared artifacts from measured results and my learning completion.
Verify sources and do not invent metrics, citations, papers, people or outcomes.
Do not execute external communications, publication or spending unless the
current session already authorizes the specific action.
Finish by updating evidence/review and TODAY with the next physical action.

v6 reproduces this brief unchanged inside v6 · Week 1 · A tank in 80 lines. The following is source planning text, not a claim of completion. Apply the explicit evidence corrections in this specification and DECISIONS.md.

Week 1 · A tank in 80 lines

• Goal: a fed-batch simulator that streams like a plant.
• Tue (B1): Biology 2e ch. 3 + 6.5. Notes.
• Thu (M1): 3Blue1Brown linear algebra ch. 1-7. Notes. Last 30 min: founder paper 1 (Michroma or Levya author).
• Build: [ ] Mon: batchlens/sim/monod.py: ODEs for biomass X, substrate S, product P, dissolved oxygen; batch and fed-batch. Done when a plot shows lag, log, stationary. [ ] Wed: MQTT publisher (paho-mqtt) sending one sample per simulated minute to the Debian box; InfluxDB + Grafana dashboard. Done when you see the curve live. [ ] Fri: 20 batches with random noise and one injected fault (feed pump stops); saved as CSV. Done when make sim writes 20 files.
• Sun: L-Sun: "A fermentation in 80 lines, streamed like a plant" with the Grafana screenshot. L-Wed (Wednesday): "what a blind bioreactor costs" (archetype note).
• Only one thing: monod.py with the plot.