Eliminating implausible fisheries assessment models using fishers’ knowledge
Bibliographic record
Abstract
Peer review of competing deepwater redfish (Sebastes mentella) assessment models revealed data inconsistencies where stock biomass decline shown by the survey in the 1990s was too rapid to be explained solely by reported catch. The models invoked different techniques to achieve fits, one by changing mortality at age and the other by survey weighting. The former fitted reported catch well, while the latter accepted a mismatch between reported and estimated catch. The assessments produced different estimates of historical stock size and future productivity. Interviews conducted with fishers of the stock suggested that catch was at least twice as high as the official record. In light of the fishers’ evidence, the model that invoked a large change in mortality with age to follow reported catch closely now appears less credible. This serves as a warning against introducing new biological mechanisms without credible justification. This is an example of how indicators derived from fisher’s knowledge, even if only from a small number of interviews, can be used to eliminate less plausible models.
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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 | 0.095 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".