**Overall Verdict:** Strong Accept — The submission is complete, professionally constructed, objectively evaluable, and reachable end to end with strong provenance and anti-shortcut controls.

**Completeness Check:**
- Instruction: Complete — Defines the corpus, forensic objective, evidence standard, prohibited conduct, required outputs, and offline constraint.
- Inputs: Complete — Includes 60 valid RGB PNG figure sheets across 10 studies, consistent manifests and captions, study context, ontology, and output schema.
- Toolchain: Complete — Uses legally obtainable open-source Python image-analysis libraries on Linux with compatible version requirements.
- Reference output: Complete — Supplies six consistent findings, normalized evidence regions, metrics, ranking, report, provenance, hashes, and evaluator source.
- Rubric: Complete — Allocates all 100 points, emphasizes independently recomputed pixel evidence, and explicitly handles false positives and severity calibration.

**File and Task Reachability:**
Attachment classification: `reachable`. Task classification: `reachable`. Both `SciIntegrityBench_ALE_INPUT_FINAL.zip` and `SciIntegrityBench_ALE_OUTPUT_FINAL_v2.zip` were inspected. They are valid, unencrypted ZIP archives of the declared sizes, contain 75 and 21 regular-file members respectively, pass archive integrity checks, have no unsafe paths, and show no excessive expansion.

The input task, ontology, corpus manifest, captions, output schema, all study-context files, and all 60 PNGs were inspected structurally; representative positive and control figure sheets were also inspected visually. All image identifiers align across the manifest, captions, and reference hashes, and all 60 staged-image hashes match. The reference JSON, JSONL, CSV, Markdown, and Python source files were inspected, including the validation, scoring, forensic-comparison, provenance, attribution, transform, ground-truth, and anti-shortcut materials. The redundant compiled Python cache was inventoried but not executed or semantically relied upon. The reviewer environment lacked Python, so the uploaded evaluator was source-reviewed rather than rerun; this is a reviewer-tooling limitation, not a submission defect.

The completion path is clear: inspect the staged images offline, emit the five schema-defined artifacts, validate their structure, and score claimed regions by recropping the frozen images and recomputing transform-aware similarity. Reference records contain six unique findings with valid boxes and numeric metrics, and the supplied reference score is consistent with the stated rubric. 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:**
`public_benchmark` — The underlying published figures and later verification notices are publicly sourced according to the submitted provenance. Risk is materially mitigated by private randomized aliases, fresh pixel transformations, an offline solver, matched controls, and a rubric assigning 92% of credit to independently verified localization on the transformed pixels. No affirmative evidence supports `ai_synthetic` or `synthetic_defect_fixture`.

**Recommended Changes:**
The submission provides strong provenance, complete solver inputs, calibrated ground truth, and a defensible behavioral evaluator. No changes are required.