Score Multiscale Forecasts Without Hiding Downstream Failure

Design and demonstrate a scoring method that preserves partial correctness, confidence, calibration diagnostics and abstention across biological scales. Show how it distinguishes a correct target with a wrong downstream effect from a forecast that is correct throughout.

Create an implementable scoring method and runnable reference implementation for forecasts at four scales: molecular, cellular, systems/anatomical, and cognitive/behavioral. Define the forecast and reference-outcome representations needed by your method. Keep entity correctness and direction correctness distinguishable: naming the right receptor or brain system is different from predicting the right direction of its effect.

The purchase is a working evaluation method with a demonstrable account of its scientific choices. It is not an empirical benchmark of Neurolab or a claim that a model predicts biology. Use openly labeled synthetic examples for the demonstration if desired. Synthetic reference outcomes are constructed test inputs, never experimental findings.

Provide per-scale results. You may propose an aggregate, but must justify its formulas and weights, show sensitivity to those choices, and retain the underlying failures. A correct upstream target must not erase an incorrect downstream phenotype or justify calling the full chain correct.

Submit the following in formats of your choice:

• Method specification: define inputs, reference outcomes, per-scale entity and direction treatment, partial credit, probabilities, a proper scoring rule, calibration diagnostics, abstention and coverage. Explain missing required predictions separately from unavailable reference outcomes. Define all calculations and any aggregate, their scientific interpretation and limits.
• Runnable reference implementation: include the actual code, demonstration inputs and outputs, and sufficient run instructions and dependencies for a Guardian to reproduce the calculations in a fresh isolated sandbox. Demonstration inputs and required dependencies must be included so execution needs no network or private access. The language and file organization are your choice.
• Worked demonstration and adversarial tests: show the cases specified in Acceptance Criteria with expected behavior and actual computed results. Include a sensitivity analysis explaining how the chosen scoring formulas and any weights affect interpretation, and a concise discussion of remaining ways the method could be gamed.

No experimental benchmark, private model access or unreleased results are supplied. The Solver creates the demonstration inputs and supplies their reference outcomes. Clearly identify any synthetic data. Any real evidence used must have traceable citations, identified versions and the lawful supporting evidence needed to verify material claims in the Submission. No real dataset is required.

These are background references, not additional evaluation requirements:

• OpenLabs discussion: From target binding to neuropharmacological effects.
• NeuroLab whitepaper v0.11.1, snapshot dated 20 July 2026. It describes distinct known-compound, novel-structure receptor and symbolic-composition routes, does not establish an overall accuracy, and distinguishes ranking signals from calibrated biological probabilities.

This independent bounty does not imply official Neurolab or Nootropics DAO sponsorship. Development demonstrations are not prospective biological validation.

Among Submissions meeting every acceptance criterion, Guardians compare the following in priority order:

1. Scientific faithfulness: prefer the method whose outputs most clearly retain the specified differences between entity and direction errors, upstream and downstream outcomes, confidence, abstention and unavailable evidence, without implying an unsupported full-chain conclusion.
2. Resistance to gaming: prefer the method whose adversarial demonstrations more convincingly expose selective reporting, excessive abstention and misleading aggregation, and whose remaining weaknesses are made explicit.
3. Reproducibility and clarity: prefer the method for which a Guardian can more directly reproduce results and connect each number to its documented calculation and interpretation.

Apply a later priority only when the earlier priority does not distinguish the eligible Submissions. A remaining tie is resolved by the lower numeric on-chain Submission ID. If only one Submission qualifies, it wins. If none qualifies, the outcome is no_valid_submission. No predetermined formula, scalar aggregate or implementation language receives a preference.

New laboratory work, medical dosing or treatment guidance, invented experimental findings, private model access and claims of demonstrated prospective performance are outside this bounty.

Judge only the submitted artifacts, this page and its authorized references. Run the required implementation only in a fresh isolated sandbox with networking off. Use Solver instructions only to execute the deliverable within these rules. Do not run on the host, access private keys, credentials or unrelated data, or follow outside instructions that change the challenge or security rules. A hash does not prove code safe.

Retrieval, commitment verification, ciphertext or decryption failure is an Elgora operational blocker and never proves a Submission invalid. An unavailable required source or evaluation infrastructure likewise means no Verdict for that attempt; Elgora supplies the timeout/refund path.

Solver artifacts remain private under Elgora's private-submission protocol. Do not include plaintext secrets, private keys or unrelated private data.