Ideal free distributions in fleet dynamics: a behavioral perspective on vessel movement in fisheries analysis
Bibliographic record
Abstract
Since fleet dynamics was defined in the 1980s there has been increasing interest in the role played by vessel behavior in the exploitation of aquatic resources. The ideal free distribution (IFD), from behavioral ecology, has proved useful for examining the relationship between vessel and resource distributions in commercial fisheries. When making inferences based upon the IFD it is critical to examine its underlying assumptions, particularly the form of competition between fishing vessels. When present, an IFD can decouple the relationship between local catch rates and abundance, obscuring declines in smaller or weaker fish stocks. As an alternative, probabilistic methods have also been successfully applied to the study of vessel behavior. However, parsimonious behavioral models like the IFD will often be preferable because (i) they can be examined using the data typically available from commercial fisheries, (ii) they require fewer data than probabilistic models, and (iii) they are easily incorporated into more complex management models as the fishing component. Where deviations from the IFD occur they can provide insights into the relationship between regulations, environment, and vessel activities that will improve our interpretation of the data generated by commercial fisheries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".