Retrospective forecasting — evaluating performance of stock projections for New England groundfish stocks
Bibliographic record
Abstract
Projections are used to explore scenarios for catch advice and rebuilding and are an important tool for sustainably managing fisheries. We tested each projection specification for 12 groundfish stocks in the Northwest Atlantic to identify sources of bias and evaluate techniques for reducing bias. Projections were made from assessments using virtual population analysis (VPA) with 1–7 years of recent data removed from the full time series and were then compared with results from a VPA assessment on the full time series of data. The main source of bias in projections was the assessment model estimates of the numbers at age in the terminal model year + 1 (Na,T+1). Recruitment was responsible for more bias in projections beyond 3 years, when population numbers begin to be dominated by cohorts that were statistically generated. Retrospective analysis was performed and several adjustment factors to reduce bias were tested. Even after adjusting for bias, the remaining bias in projections was non-negligible. The direction of bias generally resulted in projected spawning stock biomass (SSB) and catch being overestimated, and the bias in catch was nearly always larger than in SSB. Scientists need to clearly communicate the direction and magnitude of this bias, managers need to consider this additional uncertainty when specifying future catch limits, and both scientists and managers need to develop more robust control rules so that objectives are achieved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".