Hunting patterns and geographic profiling of white shark predation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Predators can play important roles in structuring their communities through top‐down effects on the distribution and abundance of their prey. Sharks are top predators in many marine communities, yet few studies have quantified those factors influencing their distribution and hunting behaviour. Here, we use location data from 340 predatory interactions between white sharks Carcharodon carcharias (Linnaeus), and Cape fur seals Arctocephalus pusillus pusillus (Schreber), data on associated environmental factors, and spatial analysis, including a novel application of geographic profiling – a tool originally developed to analyse serial crime – to investigate spatial patterns of shark attack and search behaviour at Seal Island in False Bay, South Africa. We found that spatial patterns of shark predation at this site are nonrandom. Sharks appear to possess a well‐defined search base or anchor point, located 100 m seaward of the seal's primary island entry–exit point. This location is not where chances of intercepting seals are greatest and we propose it may represent a balance among prey detection, capture rates, and competition. Smaller sharks exhibit more dispersed prey search patterns and have lower predatory success rates than larger conspecifics, suggesting possible refinement of hunting strategy with experience or competitive exclusion of smaller sharks from the most profitable hunting locations. As many of the features of this system will be common to other instances of foraging, our conclusions and approach employed may have implications and applications for understanding how large predators hunt and for studying other predator–prey systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it