What is the added value of including fleet dynamics processes in fisheries models?
Bibliographic record
Abstract
We develop a spatially and seasonally explicit bioeconomic model with three fleet dynamics processes built in endogenously. The model has been applied to the large French trawlers harvesting a medium-depth demersal stock, North Sea saithe (i.e., pollock, Pollachius virens), and a mix of deepwater species over a 10-year period (1999–2008), and the predictions have been contrasted with observations. The best overall fit was achieved where effort allocation was determined to be 80% by traditions and 20% by economic opportunism and where harvest efficiency increased by 8% a year. With this fleet dynamics parameterization, annual trends in fishing effort and profit were well reproduced by the model over the whole time period. Time series of the observed fishing effort by métier were generally well fitted by the model over the period 1999–2003, but less so over 2004–2008. The model also reasonably reproduced the catches by species over most of the time series, except for black scabbardfish (Aphanopus carbo).
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".