Behavioural biases in multispecies commercial fisheries and their impacts on stock assessments
Bibliographic record
Abstract
Accurate population estimates are a central aspect in species management, especially for fish stocks subjected to harvest pressures. Commercial fisheries catch landings provide an abundant source of data, but are inherently biased due to fishers actively targeting or avoiding certain species and areas. I investigated how biases in the behaviour of the fishers may impact the overall abundance index through generalized additive mixed models using subsets of data selected based on the inferred behaviour of the fishers for two commercially valuable species on the east coast of Canada. For both haddock (Melanogrammus aeglefinus) and redfish (Sebastes spp), fishing sets targeting the species and sets where the species were caught as bycatch produced different relative abundance indices despite being drawn from the same underlying population. When these indices were used in a virtual population analysis stock assessment for haddock, the resulting spawning stock biomass estimates reflected the biases in the index. Indices produced from bycatch data provided a more robust population estimate than target data, and may be a suitable alternative for when survey data are unavailable.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".