Increasing Occurrence of Atlantic Bluefin Tuna on Atlantic Herring Spawning Grounds: A Signal of Escalating Pelagic Predator–Prey Interaction?
Bibliographic record
Abstract
Abstract Predation can be a significant source of natural mortality for small pelagic fish species, rivaling or exceeding fishery removals. Failure to account for changes in natural mortality can introduce uncertainty in the assessment and management of these stocks. In this study, a 10-year span of hydroacoustic data was used to detect Bluefin Tuna Thunnus thynnus on two major fall spawning grounds of Atlantic Herring Clupea harengus, an economically and ecologically valuable forage fish species in the southern Gulf of St. Lawrence (sGSL). Average Bluefin Tuna detections increased 22-fold from 2002 to 2012 on both spawning grounds independently of Atlantic Herring density or aggregation size. This increase is directionally consistent but larger than changes in other Bluefin Tuna population indices. Preliminary estimates of annual Atlantic Herring consumption doubled across the time series, reaching values of 4,300–20,000 metric tons in recent years. This would suggest that Bluefin Tuna are among the most important consumers of Atlantic Herring in the sGSL. These findings are key for an ecosystem-based approach to the assessment and management of both Atlantic Herring and Bluefin Tuna in the sGSL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".