An empirical model of seasonal depth-dependent fish assemblage structure to predict the species composition of mixed catches
Bibliographic record
Abstract
Reliable catch statistics are essential for assessing fishing impacts on individual stocks. However, fisheries that capture a mixture of stocks or species for which catch statistics are not disaggregated pose a challenge. Nonetheless, catch composition can be inferred given information on fishing date and location and a prevalent role of season and habitat in structuring fish assemblage composition. Here, a harmonic regression model for multinomial data, intended to predict the species composition of catches based on season and depth, is developed using bottom-trawl survey data. Model development was motivated by the need to quantify catches of individual skate (Rajidae) species in fisheries for which landing and discard data are only reliable at the family level. The model was validated by applying it to flatfishes (Pleuronectidae), whose catches are generally reliably and consistently disaggregated by species. The predicted species composition of flatfish matched the composition observed in fishery catches well. The present approach should be applicable to other well-surveyed ecosystems where assemblage composition is structured by one or more key environmental variables of known spatial distribution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".