Spatiotemporal occurrence of loggerhead turtles (<i>Caretta caretta</i>) on the West Florida Shelf and apparent overlap with a commercial fishery
Bibliographic record
Abstract
Information on the spatial and temporal distribution of protected marine species is critical for the development of conservation strategies. We examined a 12-year dataset describing the postnesting residence areas of 81 adult female loggerhead turtles (Caretta caretta) on the West Florida Shelf (WFS) in the northeastern Gulf of Mexico. The aggregation of loggerheads on the WFS represents at least four US recovery units for this protected species. We identified several seasonally persistent residence areas that were shared by multiple loggerheads on the WFS. The majority (69%) of individuals remained within a discrete residence area throughout the tracking period. We placed our results within the context of a related fishery management concern — loggerhead bycatch within the bottom longline component of the Gulf commercial reef-fish fishery. We characterized loggerhead residence areas and compared that information with fishing activity. Our results provide information on the distribution of WFS loggerhead residence areas and the extent to which residence areas overlap with areas of high fishing effort. Loggerheads were present year-round on portions of the WFS, within or near to areas with high fishing effort. Interactions among loggerheads and fishing activities could be reduced by conservation management strategies that consider these spatial and seasonal patterns of occurrence.
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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 | 0.001 |
| 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".