Does the grey nurse shark (Carcharias taurus) exhibit agonistic pectoral fin depression? A stereo-video photogrammetric assessment off eastern Australia
Bibliographic record
Abstract
Underwater stereo-video photogrammetry was used to document the pectoral fin positions of various life-history stages of the critically endangered east Australian population of the grey nurse shark (Carcharias taurus) during normal swimming behaviour at multiple aggregation sites. A wide range in pectoral fin positions was recorded with dihedral pectoral fin angles ranging from –25 to 88°. Pectoral fin angles varied significantly among sites and this was attributed to the differing navigational and energetic requirements of the sharks. There was no significant relationship between pectoral fin angles and distances separating the shark and scuba diver. The wide range in pectoral fin angles, interactive use of the fins during swimming, low-energy behaviours of the sharks at aggregation sites and absence of ‘fight’ response agonistic behaviour indicated that the species does not exhibit agonistic pectoral fin depression. Reports of agonistic pectoral fin depression in the grey nurse shark obtained with visual estimates should be treated as preliminary observations requiring further testing using accurate sampling methods such as stereo photogrammetry. It is important that diver compliance with existing management guidelines that prohibit divers from chasing or harassing grey nurse sharks and blocking cave and gutter entrances is maintained.
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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 | 0.000 |
| 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) | 0.001 | 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".