Designed reproducible EPH income modeling lab

• Day: 2026-05-17
• Time: 11:10 to 11:20
• Project: Dev
• Workspace: WP 1: Strategic / Growth & Development
• Status: Completed
• Priority: HIGH
• Assignee: Matías Nehuen Iglesias
• Tags: Codex, Reproducibility, Machine-Learning, Agents.Md, Experiment-Design, Leakage-Control

Description

Session Goal

Define a reproducible, auditable workflow for migrating a legacy EPH income prediction effort into a modular research repository, while respecting Codex as a scarce execution resource.

Key Activities

• Separated two concerns: verifying Codex’s real operational limits from current documentation, and designing work packages to avoid vague, wasteful requests.
• Proposed a phased execution plan for the repository, organized into ordered waves:
  1. scaffold and contracts
  2. dataset builder
  3. split registry
  4. baseline experiment runner
  5. reporting artifacts
  6. scientific audit
• Defined anti-waste rules and an AGENTS.md policy to keep tasks spec-driven, reproducible, and leakage-aware.
• Reframed the repository as a small research organization / laboratory rather than a simple model-running project, emphasizing end-to-end traceability from data to thesis outputs.
• Established the central experimental focus around comparing models on log10(P47T) with full provenance and controlled validation.

Achievements

• Clarified the project architecture as a modular research pipeline with explicit contracts, experiment registry, reporting, and audit layers.
• Identified the operational intent: use Codex only for well-scoped, high-value tasks and avoid open-ended prompting.
• Consolidated the scientific scope and reproducibility requirements into a repository-level operating model suitable for future automation and thesis work.

Pending Tasks

• Validate the actual current Codex usage limits against up-to-date documentation.
• Implement the repository scaffold and AGENTS.md policy.
• Build the dataset builder, split registry, and baseline runner in the planned sequence.
• Add automated reporting and a scientific audit layer to ensure leakage control and reproducibility.

Evidence

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• event_ids: []