I still call Australia home: Satellite telemetry informs the protection of flatback turtles in Western Australian waters
Bibliographic record
Abstract
Abstract Flatback turtles (Natator depressus) are endemic to northern Australia, but their movements at sea have remained understudied. Here, we compiled one of the world's largest single‐species satellite tracking datasets (n = 280 transmitters, deployed between 2005 and 2020) to investigate the movements and level of spatial protection afforded to five flatback genetic stocks across Western Australia during different behavioral phases (i.e., inter‐nesting, migration, and foraging). Flatbacks spent 99.5% of their time in Australian waters and are provided with a very high level of spatial protection (>98% overlap with Biologically Important Areas) during the inter‐nesting phase of their life cycle. Up to 85.6% and 59.1% overlap between marine reserves and the foraging and migratory ranges for flatback stocks, respectively, was found. However, our results identified additional foraging and migratory areas where protective measures would benefit multiple stocks at once. The detailed flatback distribution maps produced here will be key resources for managers and researchers and highlight the benefits of collaborative multi‐agency studies. Additionally, this work provides a useful analytical framework for future studies endeavoring to complete large‐scale, multi‐stock spatial distributions and overlap assessments for populations of conservation concern.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".