Sizing up the ecological role of sharks as predators
Bibliographic record
Abstract
Top predators and large-bodied megafauna are often the most affected elements of exploited ecosystems, both on land and in the sea; and the top-down control these predators exert on prey species can significantly alter community structure The loss of predation and the resultant indirect effects of mesopredator release and trophic cascades have resulted in widespread trophic downgrading of ecosystems For example, both the loss of wolves from Yellowstone National Park in the USA and declines in largebodied shark populations of the western North Atlantic Ocean have been reported to cause mesopredator release and trophic cascades Recently, much of the research focus has been on the response of prey to predators; here, we focus our attention on the ecological role of the predator. Despite some compelling and widely-cited case studies, our understanding of the dynamics of predators in regulating prey populations is still limited, yet predation is recognised as a key ecological and evolutionary process Particularly in the marine realm, gaining sufficient knowledge of large, highly mobile predators to define their roles is challenging. The widespread nature of these species makes them difficult to target, handle and experimentally manipulate for the purposes of research. Thus, defining the impacts of large marine predators is problematic.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".