Can peptide-assay conclusions survive a complete provenance and signed-report audit?

Audit the full published peptide-assay release and trace its conclusions to the original source records and an explicitly scoped Solver signature. Numerical quality and valid-looking reports cannot substitute for truthful evidence attribution.

Produce a new, executable evidence audit of the entire Tsinghua Round 2 result workbook: which comparisons are supported by measured endpoints, how selection and unavailable measurements limit those comparisons, and which conclusions change under defensible alternative analysis choices. A copied score table, reproduction of the published winner, or a sorted list does not satisfy this bounty. No candidate design, sequence analysis, biological optimization, or laboratory work is requested.

This purchases analysis of a fixed published record. There are 1,522 data rows, not 1,522 demonstrated independent experiments. The workbook supplies endpoint summaries and computational annotations; it does not supply raw dose-response curves, experimental replicate identities, measurement errors, or the original participant packages. Do not invent these or present this work as replication of the original experiment.

All roles obtain peptide_round2.xlsx by public HTTPS GET without credentials from https://www.fbs.frcbs.tsinghua.edu.cn/2025-Peptide-Design-Round-2-Result4.xlsx . Its required SHA-256 is 4f861629a8ed79038e181c76f70ad6a13fe539fcc05afe500db34f2ce842cb3f. It is the sole external input. Hash mismatch or unavailability blocks judging; do not substitute another workbook.

Read cached values, never execute workbook formulas or macros. Use all rows 2–1523 inclusive in sheet Round 2; audit every remaining sheet and report its sheet name and the count of cells whose data_only=True cached cell value is not None. This count treats any present cached value as content; a formula with no cached value contributes zero. Never execute a formula to fill its missing cache. Identify records by original worksheet row. Do not extract, analyze, or submit column G (sequence), or names from B, C, and E. Column D may be used only as a published team grouping for dependence sensitivity, never as proof of experimental batch or participant identity. Do not output team names. Assign integer group IDs starting at 1 in ascending order of first worksheet-row occurrence of each distinct nonempty cached D value. Compare the original cached text exactly, without case folding or trimming; a missing value or text containing only whitespace has no group. Repeated exact values receive the same ID.

Relevant published columns are H Synthesize, I Score, J Z_activity, K Z_selectivity, L EC50 Value on NK2R (nM), M EC50 Value on NK1R (nM), N EC50, NK1R/EC50, NK2R, O % of Maximum Activation on NK2R (Peptide (500 μM)/NKA (10 μM)), P % of Maximum Activation on NC (Peptide (500 μM)/NKA (10 μM)), Q–Y computational annotations, and Z Round. Do not expand NC into an undocumented meaning. Computational annotations are not laboratory evidence. Inspect the actual cached cell types: a cell with no cached numeric value is unavailable even if it has a numeric cell type. Preserve strings and errors as missingness categories rather than converting them to zero. Boolean is not numeric. A numeric observation is a finite cached number; EC50 ratios require strictly positive numeric L and M. Unknown thresholds, censored values, and absent measurements must remain distinct from measured failures.

1. Complete evidence accounting. Produce one row-level audit for every worksheet row, with source cell addresses, availability/type categories for H–P, available computational-field counts (among Q–Y, count only finite numeric cached values, excluding booleans; strings, errors, nonfinite values and absent caches are unavailable, and retain their separate type categories), and explicit numeric or unavailable endpoint status. Summarize missingness patterns across all 1,522 rows and within every observed H and Z category. Group H and Z separately by cached value and kind: missing values, errors, booleans, finite numbers, nonfinite numbers and strings remain distinct. Within a kind compare finite numbers by numeric value and strings/errors by exact cached text. Preserve string whitespace and case; whitespace-only strings are not missing. Category labels may differ, but their source-row memberships must match these rules. Show denominators. Do not infer that a synthesis label proves a completed assay. Explain which joint endpoint analyses use smaller subsets and quantify every exclusion.

2. Consistency and interpretation. The primary eligible subset is exactly rows with finite numeric L>0 and M>0; eligibility never requires N, I, or any synthesis/round label. For those rows calculate M/L and L/M independently. Compare either ratio with N only when N is finite numeric; an unavailable N leaves the computed ratio intact but makes its comparison null with a reason. Independently calculate J+2K whenever both J and K are finite numeric, regardless of L/M eligibility, and compare with I only when I is finite numeric; an unavailable I leaves the computed sum intact but makes its comparison null with a reason. Use computed value minus the published comparison value for each signed difference. Report signed differences, absolute differences, and discrepancies using abs(a-b) > 1e-6 * max(1, abs(a), abs(b)). These tolerances are arithmetic comparison conventions, not assay-validity thresholds. Account separately for unavailable comparisons. Quantify how often published ratios agree with each direction and never silently choose the direction that gives a preferred result. Use M/L as the primary descriptive ratio in this audit; the reciprocal remains an explicit sensitivity analysis. Identify what cannot be reconstructed without raw measurements or the original standardization population.

3. Sensitivity of population-level conclusions. On rows with positive numeric L and M, report medians and interquartile ranges of log10(L), log10(M), and log10(M/L), and Spearman correlation between log10(L) and log10(M), using average ranks for ties. Repeat on two separate subsets derived directly from the primary eligible subset: first retain rows whose I score is finite numeric (including zero or negative scores; excluding booleans, strings, errors and absent caches). Second, compute the 5th and 95th percentiles independently for the unlogged L values and unlogged M values across the full primary eligible subset, then retain a row only when both L and M lie within their respective inclusive intervals. Do not apply the score restriction before this separate trimming scenario, trim logged values, or recompute thresholds after exclusions. This trimming is a sensitivity scenario, not evidence that excluded observations are invalid. For every distribution summary use numpy.median(finite_values) for the median and numpy.quantile(finite_values, [0.25, 0.75], method="linear") for Q1 and Q3; IQR is Q3-Q1. Empty finite sets yield null statistics with an explicit reason. Other empirical quantiles, including the 5th/95th-percentile trimming endpoints, use numpy.quantile(..., method="linear"). For each comparison report eligible row IDs, sample size, numeric result or the exact reason it is undefined. Correlation is undefined with fewer than three pairs or either constant ranked variable. Also report median and interquartile range of each available O and P endpoint, and pairwise Spearman correlations of O with P, O with log10(L), and P with log10(L), using only finite paired values and positive L when logged. Include all pairwise denominators and missingness; do not interpret the labels as independently validated biological measurements.

4. Uncertainty without invented assay errors. Compute 1,000 percentile bootstrap resamples for the three medians and the correlation above in the primary eligible subset, using NumPy Generator(PCG64(20260909)), starting with ascending worksheet-row order and resampling whole rows with replacement and keeping paired endpoints together. Report the 2.5th and 97.5th percentiles of finite results using numpy.percentile(values, [2.5, 97.5], method="linear"), valid-resample counts, and all undefined reasons. If no finite resample results exist for a statistic, both interval endpoints are null with that reason. If at least three distinct nonempty team groups occur in the eligible subset, repeat with a fresh generator using the same seed, by sampling the observed groups in ascending group-ID order with replacement and retaining all eligible rows in each sampled group; exclude rows without a group only from this grouping sensitivity and count them. Otherwise explain why this second procedure is unavailable. These are conditional resampling intervals over the observed record, not measurement confidence intervals or population guarantees; team grouping does not establish independent laboratory replicates.

5. Selection-bias and robustness analysis. For the descriptive condition M/L > 1, give the count and fraction among eligible paired rows. Across all 1,522 rows calculate the sharp missing-outcome range [p/1522, (p+u)/1522], where p counts observed eligible rows satisfying the condition and u is every row without an eligible pair. This condition compares recorded endpoints, not a clinical-success threshold. Explain why narrowing that range requires unsupported assumptions. Recalculate the primary three medians and condition fraction under five named perturbations: multiply every eligible L by 0.9, by 1.1, multiply every eligible M by 0.9, by 1.1, and swap L with M. Keep eligibility fixed before these perturbations. Report changes and explicitly identify these as hypothetical sensitivity calculations, not known measurement errors.

6. Evidence-backed conclusions. Give at least one numerical conclusion from each of accounting, consistency, uncertainty, and sensitivity, linking each to an output table and worksheet cells. For each state whether it describes the observed subset, depends on an assumption, or is not identifiable. Explicitly address raw-assay quality, independence, selection into endpoint measurement, score comparability, and why this record cannot establish clinical effectiveness. A correct conclusion of non-identifiability passes when accompanied by the required numerical analysis and the specific missing evidence; using that phrase to skip available analyses fails.

Include RUN.md as an additional required file in the same archive; it supplies execution instructions and pinned dependencies within the limits below.

Submit one ZIP, at most 20 MB compressed and 50 MB uncompressed, containing report.md (at most 20,000 words), analysis.py, methods.json, results/ with machine-readable JSON or CSV tables for all six analyses, and up to 12 PNG figures. Do not include source data, cached results as executable input, sequence material, participant names, downloaded libraries, symlinks, executables other than readable Python source, or additional archives. Tables must preserve worksheet-row provenance. methods.json lists each output file, columns/types, calculations, missing/undefined meanings, quantile convention, seed, grouping choices, and figure-to-table mapping. No unstated discretionary threshold may affect a required result.

Guardians download and verify the fixed workbook, then perform one complete reproduction using only that input. The numerical reference versions are Python 3.12, NumPy 2.2.6, pandas 2.2.3, SciPy 1.15.3, openpyxl 3.1.5 and Matplotlib 3.10.3. Include RUN.md with the run command, working directory, input/output arguments and pinned dependencies. Regenerate every submitted result table into an empty output directory without using submitted results as input. The evaluation budget is at most 4 CPU cores, 8 GB RAM, 1 GB temporary disk and 30 minutes wall time. One additional run is permitted only after a documented infrastructure interruption. Each Guardian owns its security and execution setup under Elgora rules; these limits bound the evaluation, not the setup.

This challenge accepts analysis of the original published peptide-assay record. It does not accept a claim that the Solver performed those experiments. Published endpoint values are evidence of what the publisher reported, not independent proof of physical sample custody, assay validity or new laboratory work. No laboratory signing key is supplied by this challenge. A Solver signature identifies the author of this analysis; it is never a laboratory attestation.

The fixed workbook, exact source URL and pinned digest above are the authority for source-record identity. A checksum supplied only by the Solver, a screenshot, a copied logo or a self-issued certificate is insufficient. Guardians independently retrieve the fixed publisher input and check its digest. The worksheet row within that exact workbook identifies the released candidate record; it does not establish the identity of a physical sample. No Solver may substitute a different row, release, experiment or candidate merely because its numbers are similar.

In addition to the baseline files, include:

• provenance.csv: one row for every original worksheet row 2–1523. Columns: source_sha256, sheet, worksheet_row, source_cells, result_references, evidence_scope. The two reference fields contain JSON arrays of cell addresses and relative result-file/record references. Each row must link all its reported observations to their actual original cells; aggregates must preserve their complete contributing row set in the result tables. Use publisher_reported_historical_data as the scope. Do not include excluded names or sequences.
• attestation.txt: the exact UTF-8 statement described below, with LF line endings and one final newline.
• attestation.sig: the 0x-prefixed hex EIP-191 personal-message signature over the bytes of attestation.txt. The recovered address must equal the active on-chain Solver address for this Submission. A submission transaction by itself does not replace this signed statement.

The first six lines of attestation.txt, in this order, are:

Elgora historical evidence analysis v1
chain_id=<decimal chain id>
bounty_id=<decimal bounty id>
challenge_sha256=<lowercase SHA-256 of the exact published challenge bytes>
source_sha256=4f861629a8ed79038e181c76f70ad6a13fe539fcc05afe500db34f2ce842cb3f
statement=I authored this analysis of publisher-reported historical data; I do not claim that I or this signature attest to the original laboratory work.

Append one line for every other regular payload file in the ZIP, excluding attestation.txt and attestation.sig: <lowercase SHA-256><two spaces><relative path>. Sort by relative path in UTF-8 byte order. Paths are relative to the ZIP root, use /, and must not contain newline, carriage return, . or .. path components. Duplicate paths, unlisted payload files, extra listed files or a digest mismatch fail this requirement. Sign the UTF-8 text as a personal message, not a hex-decoded digest. The ZIP container itself is not listed, avoiding a circular hash. The challenge SHA-256 is an additional explicit binding; it does not replace Elgora's own challenge commitment.

These three provenance files are the only added exceptions to the baseline file list, along with the quality-extension files explicitly permitted below. The attestation is checked against the submitted payload; it is not regenerated during scientific reproduction and private signing keys must never be submitted. The supplied run command must regenerate provenance.csv along with the analytical outputs. Verify the signature outside execution of Solver code using an independent EIP-191 implementation. Every payload hash and source link must be checked, not a sample.

Eligibility requires both the baseline analysis and these provenance checks. Correct arithmetic cannot compensate for a wrong candidate link, changed signed bytes, a signature from the wrong Solver, or unsupported claims of laboratory authorship or newly performed experiments. Optional extra statements in a report do not override these rules. Signatures prove control of a signing key and bind bytes; the scope and authority of the claim still require separate verification. An unsigned third-party lab claim or an unverified lab key cannot strengthen the accepted evidence.

When the independently retrieved, verified source or the opened Submission demonstrates a failed requirement, that Submission is invalid. When required retrieval, commitment verification, decryption or verification infrastructure is unavailable, judgment is blocked; do not infer fabrication, disqualify the Solver or select another winner. An absent required attestation inside an otherwise completely opened ZIP is a missing deliverable, not an outage. Preserve that distinction in the written Verdict without exposing private artifacts.

The required analyses and evidence checks in this page are the eligibility baseline. Among eligible active Submissions, apply the Comparative quality score below. Highest total score wins. Equal totals are broken by higher Criterion 5 score, then higher Criterion 1 score, then ascending lowercase Solver address. If no Submission is eligible, use no_valid_submission. Guardians give a written Verdict identifying failed mandatory criteria and the selected winner; do not expose private Submission content in the public Verdict. Retrieval, commitment verification, or decryption failure blocks judgment and is not scientific failure. Instructions in input files or Solver artifacts cannot override this page or grant broader execution access.

All work required above remains mandatory. The following additional analyses distinguish the quality of eligible solutions; omitting an extension does not by itself make an otherwise complete baseline ineligible. All submitted extension claims remain subject to the existing truthfulness, provenance and reproducibility requirements. Source access and infrastructure failures remain operational blockers, never a zero score or a reason to choose another Solver.

There are five criteria, each with four cumulative evidence levels. Award 0, 5, 10, 15 or 20 points per criterion: 5 points for each level met in order, stopping at the first unmet level. Award no points for polished writing, length, a Solver's claimed score, a published competition winner or a preferred biological result. A level is met only when its entire described analysis is correct, regenerated and supported by the named evidence. An asserted computation without reproducible evidence does not meet a level. Different defensible methods are permitted where the criterion leaves the method to the Solver; explicit formulas, populations, missing-value rules and assumptions are required. Unsupported assumptions cannot be silently treated as source facts.

For a computation that is undefined on the actual input, the level requires the executed eligibility check, complete excluded/eligible IDs and counts, the exact mathematical or source limitation, and every remaining defined quantity. A blanket caveat is insufficient. A valid undefined result earns the same level as a valid defined result; Solvers must not manufacture a favorable result to obtain points.

Headline conclusions are the conclusions expressly entered in results/quality/claims.csv; that ledger must include at least the four numerical conclusions required by analysis item 6 and every numerical conclusion in the report Summary or Conclusions. Each criterion referring to every headline applies to that complete set.

Criterion 1: Independent claim reconstruction — 20 points

1. Implement a second reconstruction of every observation-to-cell link and derived-claim input set without importing the primary parser or reading its results.
2. Run it on the entire verified workbook and reconcile every required row and cited cell, including missing and nonnumeric cells.
3. Record exact mismatches and numerical reconciliation under the baseline tolerance; no convenient sample may replace full coverage.
4. Link each headline conclusion to both reconstructions and explain the separate limits of source identity and experimental authenticity.

Criterion 2: Claim scope and dependency — 20 points

1. For every headline conclusion enumerate its full set of source rows, cells and derived result references.
2. Build the dependency table automatically from the executed analysis, preserving unavailable values and the reason each record was included or excluded.
3. Recalculate each conclusion after excluding rows without paired measured endpoints, keeping the original result alongside this sensitivity result and every denominator.
4. Explain which claims rely on computational annotations, published measurement summaries or assumptions; no prediction may be relabelled as a measured result.

Criterion 3: Selection and uncertainty — 20 points

1. Cross each baseline endpoint perturbation with the full paired, finite-score and trimmed populations defined on the unperturbed source.
2. Regenerate every applicable statistic and record the full source membership for all fifteen combinations.
3. Report the numerical range and direction of each headline statistic over all computable combinations, retaining undefined cases.
4. State which numerical conclusions remain supported across the executed choices without presenting hypothetical perturbations as additional lab measurements.

Here, support and a qualifying outcome mean the baseline descriptive condition M/L > 1. Within each exact observed H or Z category, n is its total row count, p is its eligible paired rows satisfying that condition, and u is its rows without an eligible pair. Bounds are [p/n,(p+u)/n]; use the same baseline eligibility and missingness rules. Categories are evaluated separately, not as an H-by-Z cross-product. A majority is strictly greater than n/2. These are descriptive record bounds, not assay-success criteria.

Criterion 4: Evidence-loss consequences — 20 points

1. For each observed synthesis and round category calculate the baseline no-assumption support bounds with complete counts and row membership.
2. Enumerate the possible count of qualifying missing outcomes from zero through the number of unresolved rows in that category.
3. Report the minimum count needed for a strictly greater than one-half descriptive majority, or a proved already-met or impossible result.
4. Explain what additional measurement evidence could narrow the bounds, without treating signatures or publication provenance as substitutes for measurement quality.

Criterion 5: Independent audit usability — 20 points

1. Provide an audit index connecting every mandatory output to its calculation, source coordinates, signed file digest and stated scope.
2. Make the index machine-checkable against all included payload files and every headline claim; preserve multiple source rows for aggregates.
3. Supply a second verification path for source-coordinate and digest comparisons using readable code separate from the main analysis; record both agreement and failures.
4. For every headline conclusion state what the cited calculations establish within their recorded population and assumptions, and identify a broader claim they do not establish. A claim extending to unmeasured outcomes, physical sample identity or new laboratory work is not supported by this record.

Put extension methods in quality_methods.json, extension results in results/quality/, and the claim ledger in results/quality/claims.csv. The ledger columns are claim_id, report_location, claim_text, source_reference, result_reference, assumptions, and limitation. Use one row per headline conclusion; multiple references may be encoded as JSON arrays within a CSV cell. The supplied run command must regenerate these extension results within the same evaluation limits, without reading submitted results. Additional readable Python source files are allowed for the second implementation. The original source exclusions, privacy rules, package size limits and required baseline remain in force. Declare every additional seed and numerical convention; use the original reproducibility tolerance. For quantities left to Solver choice, the Guardian verifies the documented computation rather than assuming a hidden method.

Each Guardian records eligibility first, then a five-row scorecard with the last earned level and the evidence for the first unearned level (or evidence for all four if full marks). Total points equal the sum, from 0 to 100. Apply the tie-break only when totals are exactly equal. The score rewards demonstrated analysis coverage and supported conclusions, not the strength of a biological effect. Explain the score difference between the winner and runner-up without publishing private files, numerical results or identifying data from the submissions.

AI assistance and reuse of disclosed code are permitted. Explain prior work used in the report; regenerate all required results from the fixed inputs. Identical results alone do not prove copying. No new laboratory work, candidate design or sequence optimization is purchased.