Investigating the influence of minor krill-predators on the krill-predator dynamics of the Antarctic ecosystem in the International Whaling Commission's Management Area II
Bibliographic record
Abstract
Krill ( Euphausia superba), a small pelagic crustacean. is the largest food resource serving many predators in the Antarctic ecosystem. Given the recent slow expansion of the krill fishery, interest is increasing on how best to harvest krill without unduly impacting its natural predators. The Mori–Butterworth Antarctic ecosystem model attempted to explain the dynamics of the “major” predators through predator–prey interactions alone. That model considered blue, fin, humpback, and minke whales, and crabeater and Antarctic fur seals. Here, this model is expanded to include “minor” krill-predators, such as mackerel icefish. It focuses on a smaller scale, roughly corresponding to International Whaling Commission Management Area II. Results suggest a meaningful difference (historical abundance trajectories differing by more than 5% on average over time) when including “minor” predators. Hence, at an area-specific level, “minor” predators should be considered for further models of at least certain sectors of the Antarctic, as they may meaningfully influence the dynamics of krill and its “major” predators. This in turn could impact management decisions for the krill fishery, as regards optimal krill harvesting.
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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 | 0.000 | 0.000 |
| 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".