Spatial distribution of catch and effort in a fishery for snow crab (<i>Chionoecetes opilio</i>): tests of predictions of the ideal free distribution
Bibliographic record
Abstract
The ideal free distribution (IFD), a hypothesis from behavioural ecology, predicts that fishery effort should map resource distribution better than catch-per-unit-effort (CPUE) when interference competition occurs in the fishery. We tested this prediction using data from the fishery and annual research survey for snow crab (Chionoecetes opilio) in the southern Gulf of St. Lawrence. Effort was positively correlated with the local abundance of crabs in all years. Correlations between CPUE and local crab abundance were also positive in some years, but negative in others. In the latter cases, CPUE and effort were also negatively correlated, suggesting intense competition in the fishery. In most years, CPUE tended to be equalized among areas compared with the distributions of effort and local crab abundance, as predicted by the IFD. In most years, differences in spatial distribution were more significant between CPUE and crab abundance than between effort and crab abundance. Although effort was the more reliable indicator of resource distribution, even it provided a distorted view of this distribution, as predicted given expected violations of IFD assumptions. For example, effort tended to be higher than expected on fishing grounds near home ports and lower than expected on distant grounds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".