Impacts of cetaceans on the structure of Southern Ocean food webs
Bibliographic record
Abstract
Recently, Ballance et al. (2006) revived the hypothesis that cetaceans were a majorforce in the structuring of Southern Ocean food webs, and suggested that they arestill playing a keystone role even as their loss continues (see also review in Mori andButterworth 2006), a subject that we herein would like to emphasize. Accordingto this hypothesis, following 60 yr of directed industrial whaling (Tnnessen andJohnsen 1982, Baker and Clapham 2002), the demise of the great whales (blue,Balaenoptera musculus intermedia; fin, B. physalus; and humpback, Megaptera novaeangliae)led to changes in populations and demographic parameters among penguins,seals, and minke whales (B. bonaerensis; see also Laws 1977, Bengtson and Laws1985). These changes to populations of the great whales competitors came aboutupon release from trophic competition as a result of the krill surplus that ensued(i.e., of Antarctic krill, Euphausia superba; Bengtson and Laws 1985).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".