Comparative analysis of the spatial distribution of fishing effort contrasting ecological isodars and discrete choice models
Bibliographic record
Abstract
The spatial dynamics of catch and effort data are often overlooked in fisheries research despite its well-documented utility in understanding the distribution and abundance of fish. We apply a recently developed fisheries isodar model to an otter trawl groundfish fishery on the Scotian Shelf and compare its predictive performance with a more traditional discrete choice model random utility model. Isodars represent the expected distribution of foragers between two habitats when fitness is equal and can be a representation of the ideal free distribution. Here, fitness was defined with relative catch rates, cost differentials, and interference effects between habitats. Random utility models were constructed as mixed logit models to give the expected probability of fishing in a particular area based on a collection of predictors. The predictions of the isodar models consistently outperformed the mixed logit for both in-sample and out-of-sample forecasts and the isodar was determined to be the preferred model based on its increased accuracy and simplicity. The isodar model can provide a statistically powerful and easily implemented tool in effort studies, especially in situations of aggregated or limited data, which can inform conservation and management decisions.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".