Advancing the application of the ideal free distribution to spatial models of fishing effort: the isodar approach
Bibliographic record
Abstract
The ideal free distribution (IFD) of behavioral ecology has been used in the study of the distribution of fishing effort since the 1990s. Concurrently, evolutionary perspectives on forager distributions have led to the development of theoretical curves of equal fitness, named isodars, to test IFD hypotheses. We develop isodars, based upon catch rates and unknown costs, to quantify regularity in the distribution of fishing effort among alternative areas. Our analyses indicate that these isodars provide significantly better predictions than a simple IFD without costs. Autocorrelation in the catch and effort data necessitates the use of generalized linear least squares when estimating model parameters. Differences in costs that are proportional to effort are more clearly identified in the model than nonlinear effects, which may arise from extreme interference competition. The isodar approach provides a new tool for examining the spatial dynamics of catch and effort data. It improves the accuracy of predictions and provides new parameters related to costs and vessel interactions that can be applied to rapidly identify situations where effort dynamics have changed.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| 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".