Using model-based inference to evaluate global fisheries status from landings, location, and life history data
Bibliographic record
Abstract
Assessing fishery collapses worldwide is hindered by the lack of biomass data for most stocks, leading to the use of landings-based proxies or the assumption that existing stock assessments are globally representative. We argue that the use of sparse assessments to evaluate fishery status requires model-based inference because assessment availability varies spatially and temporally, and we derive a model that extrapolates from assessment results to available landings, life history, and location data. This model uses logistic regression to classify stocks into different prediction bins and estimates the probability of collapse in each using cross-validation. Results show that landings, life history, and location are informative to discriminate among different probabilities of collapse. We find little evidence that regions with fewer assessments have a greater proportion of collapsed stocks, while acknowledging weak inferential support regarding regions with one or fewer assessments. Our extrapolation suggests that 4.5%–6.5% of stocks defined by landings data are collapsed, but that this proportion is increasing. Finally, we propose a research agenda that combines stock assessment and landings databases while overcoming limitations in each.
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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 | 0.051 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".