Accounting for fish shoals in single- and multi-species survey data using mixture distribution models
Bibliographic record
Abstract
A scientific bottom trawl survey targeting Pacific rockfishes (Sebastes spp.) occasionally yields extraordinary catch events (ECEs) in which catch-per-unit-area is much greater than usual. We hypothesize that ECEs result from trawl catches of fish shoals. We developed mixture distribution models for positive catch rates to identify spatial covariates associated with ECEs or normal trawl catches and used simulation modeling to contrast the performance of mixture distribution and conventional log-linear models for estimating an annual index of positive catch rates. Finally, mixed-effects modeling was applied to multispecies data to evaluate the hypothesis that ECEs are related to shoaling behaviors. Results show that mixture distribution models are often selected over conventional models for shoaling species and that untrawlable habitat has a positive effect on rockfish densities. Simulation shows that mixture distribution models can perform as well as or better than conventional models at predicting positive catch rates. Finally, model selection supports the hypothesis that shoaling behaviors contribute to the occurrence of ECEs. We propose that greater understanding of ECEs and shoaling habitat selection could be useful in future spatial management and survey design and that mixture distribution models could improve methods for estimating annual indices of abundance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".