Fine-scale spatial association between baleen whales and forage fish in the Celtic Sea
Bibliographic record
Abstract
Baleen whales can be regularly observed in the Celtic Sea; however, little is known about their local foraging behaviour. The study objective was to determine whether or not baleen whales selectively prey upon particular forage fish species or, on the contrary, is predation on the Celtic Sea plateau driven by random encounters between prey and predator? Concurrent sighting surveys for fin (Balaenoptera physalus), minke (Balaenoptera acutorostrata), and humpback (Megaptera novaeangliae) whales were carried out simultaneously from 2007 to 2013 during dedicated fisheries acoustic surveys assessing the abundance and distribution of forage fish. Probabilities of spatial overlap between baleen whales and forage fish were analysed and compared with the probability of a random encounter. For estimations of foraging threshold and prey selectivity, mean fish biomass and fish length were calculated when baleen whales and forage fish co-occurred. Whales were dominantly observed in areas with herring (Clupea harengus) and sprat (Sprattus sprattus), while areas with mackerel (Scomber scombrus) were not targeted. A prey detection range of up to 8 km was found, which enables baleen whales to track their prey to minimize search effort. Fish densities within the defined foraging distance ranged from 0.001 to 3 kg·m−2 and were correlated to total fish abundance. No prey size selectivity according to fish length was found. By linking baleen whale distribution to high-density herring and sprat areas, it was possible to identify the Celtic Sea as a prey hot spot for baleen whales during autumn.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".