Predator–prey and competitive interactions between sharks (order Selachii) and dolphins (suborder Odontoceti): a review
Bibliographic record
Abstract
Abstract The importance of interactions between sharks and cetaceans has been a subject of much conjecture, but few studies have addressed these interactions. Sharks (order Selachii) have been hypothesized to be important predators on dolphins and porpoises (suborder Odontoceti). Unfortunately, there are often few data to back up claims that certain shark species are major threats to cetaceans. To help identify potential shark predators in specific locations, available data on interactions with odontocetes for all shark species that may include cetaceans in their diet are reviewed. Shark species are categorized into groups based on predatory interactions with dolphins and porpoises (regular predators, occasional predators, potential predators, ectoparasites and insufficient data). Several shark species that have been overlooked in the cetacean literature are identified as potentially important predators while others that have been suspected to be important predators are probably at most occasional predators. How shark predation can influence dolphin populations, habitat use, group size and behaviour is discussed. How risk of shark predation can vary with habitat attributes in both nearshore and pelagic waters is also discussed. Predator–prey interactions have been the focus of most studies of shark–dolphin interaction, but competitive interactions may also occur. The first analysis of shark–dolphin dietary overlap is presented, which shows it to be significant between common dolphins and several species of sharks, including species that prey upon these dolphins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".