A dynamic model of the Bay of Biscay pelagic fleet simulating fishing trip choice: the response to the closure of the European anchovy (Engraulis encrasicolus) fishery in 2005
Bibliographic record
Abstract
The scope of this paper is to describe, evaluate, and forecast fishing trip choices of the Bay of Biscay pelagic fleet using random utility modeling (RUM). First, alternative fishing trip choices of this fleet were identified using multivariate statistical methods based on species landings weighted by value and defined as distinct fishing activity or fisheries (termed métiers). A RUM was specified, which included trip components as attributes during the period 2001–2004 (a lagged percentage of the value per unit of effort of the main species caught, total value per unit of effort, and inertia in terms of changes from one métier to another). For the main métiers, the proportion of correct effort allocation is 90% during the calibration period of 2001–2004. The results from the RUM are used to parameterize a simulation model of trip choice. The model is used to predict trip choices in 2005, throughout most of which fishing was constrained by the closure of the European anchovy (Engraulis encrasicolus) fishery. Simulation results are compared with observed trip choices following the fishing ban: 80% of observed trip choices are correctly predicted by the model. The capacity of the behavioral model to predict responses to the closure is then discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".