Evaluating potential tagging effects on leatherback sea turtles
Bibliographic record
Abstract
Although the use of satellite tracking to study the leatherback sea turtle Dermochelys coriacea continues to increase, there has been little inquiry into the effects of this research.We investigated effects of handling and tagging on leatherbacks using state-space estimated positions from 42 turtles satellite-tagged at sea.Although a control group was not available, we observed several possible effects of tagging and handling.Turtles were much more likely to begin migration, and travel speeds were significantly higher in the first week after capture.We inferred that 17 of the 42 turtles departed Canadian waters immediately after tagging.Turtles were more likely to begin their migration immediately if they were tagged later in the year, or if they were tagged following entanglement in fishing gear.Turtles that remained in the north commenced foraging after a median of 12.7 d.We also documented reports of previously harnessed leatherbacks re-sighted on nesting beaches.Although it remains uncertain whether the observed effects are due to capture and/or tagging and whether they are detrimental to individual turtles, this study emphasizes the necessity of considering tag effects on this species.
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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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.001 |
| 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".