SHARK ATTACKS ON BOTTLENOSE DOLPHINS (<i>TURSIOPS ADUNCUS</i>) IN SHARK BAY, WESTERN AUSTRALIA: ATTACK RATE, BITE SCAR FREQUENCIES, AND ATTACK SEASONALITY
Bibliographic record
Abstract
A bstract Shark predation may have been a central factor influencing the evolution of sociality in dolphins, as well as a determinant of dolphin habitat use and behavior. To understand the role of predation in driving interpopulation differences in behavior and sociality, it is important to quantify differences in predation risk among populations. This study describes the frequency of shark‐inflicted scars and estimates the shark attack rate on bottlenose dolphins ( Tursiops aduncus ) in Shark Bay, Western Australia. Shark bite scars were found on 74.2% (95 of 128) of non‐calves, and most of these scars were inflicted by tiger sharks ( Galeocerdo cuvier ). Although there were no differences among age/sex classes in the frequency of scarring, significantly more adult males than adult females bore multiple scars. The rate of unsuccessful shark attack was estimated to be between 11% and 13% of dolphins attacked each year. Large sharks (>3 m) were responsible for a disproportionate number of attacks. However, bites from small carcharhinid sharks on 6.2% of dolphins suggest that some of these small sharks may be dolphin ectoparasites. Both the scar frequencies and attack rate suggest that Shark Bay dolphins face a greater risk of predation than bottlenose dolphins in other locations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".