Oceanic Sharks Clean at Coastal Seamount
Bibliographic record
Abstract
Interactions between pelagic thresher sharks (Alopias pelagicus) and cleaner wrasse were investigated at a seamount in the Philippines. Cleaning associations between sharks and teleosts are poorly understood, but the observable interactions seen at this site may explain why these mainly oceanic sharks regularly venture into shallow coastal waters where they are vulnerable to disturbance from human activity. From 1,230 hours of observations recorded by remote video camera between July 2005 and December 2009, 97 cleaner-thresher shark events were analyzed, 19 of which were interrupted. Observations of pelagic thresher sharks interacting with cleaners at the seamount were recorded at all times of day but their frequency declined gradually from morning until evening. Cleaners showed preferences for foraging on specific areas of a thresher shark's body. For all events combined, cleaners were observed to conduct 2,757 inspections, of which 33.9% took place on the shark's pelvis, 23.3% on the pectoral fins, 22.3% on the caudal fin, 8.6% on the body, 8.3% on the head, 2.1% on the dorsal fin, and 1.5% on the gills respectively. Cleaners did not preferentially inspect thresher sharks by time of day or by shark sex, but there was a direct correlation between the amount of time a thresher shark spent at a cleaning station and the number of inspections it received. Thresher shark clients modified their behavior by "circular-stance-swimming," presumably to facilitate cleaner inspections. The cleaner-thresher shark association reflected some of the known behavioral trends in the cleaner-reef teleost system since cleaners appeared to forage selectively on shark clients. Evidence is mounting that in addition to acting as social refuges and foraging grounds for large visiting marine predators, seamounts may also support pelagic ecology by functioning as cleaning stations for oceanic sharks and rays.
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How this classification was reachedexpand
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) | 0.030 | 0.008 |
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; both teacher heads agree on what is shown here.
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".