**Overall Verdict:** Strong Accept — The submission is complete, internally consistent, safely reviewable, professionally representative, and objectively evaluable end to end.

**Completeness Check:**
- Instruction: Complete — The modeling goal, temporal boundaries, label construction, scorable population, split, calibration requirement, and output contract are precise.
- Inputs: Complete — All three synthetic feeds and the schema are readable, checksum-valid, correctly separated from reference materials, and consistent with the stated dimensions and semantics.
- Toolchain: Complete — The specified open-source Python stack supports the full CPU-only workflow on the stated single-machine resources.
- Reference output: Complete — Scores, both model formats, the 78,864-row feature matrix, calibration table, metrics, report, and reproduction sources are present and mutually consistent.
- Rubric: Complete — The grader defines objective structural, identity, performance, calibration, leakage, security, and deterministic-regeneration checks with explicit thresholds and failure severity.

**File and Task Reachability:**
Attachment classification: reachable. Task classification: reachable. I inspected `input_materials.zip`, `reference_output.zip`, and `selftest_evidence.txt`. Both ZIP files are valid, unencrypted containers containing only regular files with safe paths; their archive hashes match the submitted values, every compressed member passes integrity testing, and every in-band manifest checksum validates.

The input archive contains only the three source CSVs, `schema.json`, and its manifest. Full profiles confirmed 3,092,581 POS records, 745,784 inventory records forming 7,171 complete 104-week SKU-DC panels, and 120,220 purchase orders, with declared schemas, plausible ranges, and no blank fields. An independent reconstruction produced exactly 78,864 test rows and 5,582 positives, with zero key, date, horizon, snapshot-ID, or label mismatches against the reference scores.

The reference archive contains 20 members. Its score metrics independently reproduce exactly: ROC-AUC 0.886315, PR-AUC 0.479982, Brier 0.048533, and equal-width ECE 0.008298. The Parquet footer confirms 78,864 rows, 51 feature columns, and zero nulls. The SKOPS schema identifies the expected calibrated scikit-learn pipeline and scientific-library state types. Uploaded programs were inspected statically but not executed, and model artifacts were not deserialized; numerical model arrays were reviewed structurally rather than value by value. No relevant attachment was left unreviewed at the member-inventory level.

The task has a coherent path from raw feeds through leakage-controlled feature engineering, temporal training and calibration, score generation, and automated evaluation. The stated CPU, memory, disk, and runtime requirements are feasible on one suitable Linux machine. License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.

**Special Screening:**
None

**Recommended Changes:**
The submission already provides complete inputs, reproducible reference artifacts, strong safety controls, and a discriminating machine-checkable rubric. No changes are required.