Temperature and depth associations of porbeagle shark (<i>Lamna nasus</i>) in the northwest Atlantic
Bibliographic record
Abstract
Abstract The porbeagle ( Lamna nasus ) is a large fast‐swimming pelagic shark found at high latitudes in both hemispheres. To examine the influence of temperature on porbeagle distribution, a detailed analysis of the relationship between catch rate, temperature, depth and location was carried out based on 420 temperature profiles taken during commercial fishing operations. More than half of the porbeagle were caught at temperatures of 5–10°C (at the depth of the hook); the mean temperature at gear of 7.4°C differed very little among seasons. Most of the spring fishing took place near fronts, although the affinity with fronts was not evident in the fall. Temperature at depth was a significant modifier of catch rate when included in a generalized linear model controlling for the effects of location, fishing vessel, month and year. However, sea surface temperature was a poor predictor of catch rate. The similarity between environmental and catch‐weighted cumulative distribution functions confirmed suggestions that fishers sought out the most appropriate temperature range in which to set their gear. As porbeagle are among the most cold tolerant of pelagic shark species, we suggest that they have evolved to take advantage of their thermoregulating capability by allowing them to seek out and feed on abundant coldwater prey in the absence of non‐thermoregulating competitors.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".