Evaluation of species distribution forecasts: a potential predictive tool for reducing incidental catch in pelagic fisheries
Bibliographic record
Abstract
Nontarget catch restrictions are becoming common in fisheries management. We test a potential tool for reducing nontargeted catch that combines species’ distribution models and ocean forecast models. We evaluated our approach for Atlantic herring (Clupea harengus), Atlantic mackerel (Scomber scombrus), alewife (Alosa pseudoharengus), and blueback herring (Alosa aestivalis). Catch of the latter two species is capped in commercial fisheries of the former two species. Ocean forecasts were derived from a data-assimilative ocean forecast model that predicts conditions 0–2 days into the future. Observed oceanographic conditions were derived from CTD casts and observed fish presence–absence was derived from fishery-independent bottom trawl collections. Species distribution models were used to predict presence–absence based on observed and forecasted oceanographic conditions, and predictions for both were very similar. Thus, most of the error in predicted distributions was generated by the species distribution models, not the oceanographic forecast model. Understanding how predictions based on forecasted conditions compare with predictions from observed conditions is key to developing an incidental catch forecast tool to help industry reduce nontarget catches.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".