A brighter future? Stable and growing sea turtle populations in the Republic of Maldives
Bibliographic record
Abstract
The Indian Ocean represents a significant data gap in the evaluation of sea turtle population status and trends. Like many small island states, the Republic of Maldives has limited baseline data, capacity and resources to gather information on sea turtle abundance, distribution and trends to evaluate their conservation status. We applied a Robust Design methodology to convert opportunistic photographic identification records into estimates of abundance and key demographic parameters for hawksbill sea turtles (Eretmochelys imbricata) and green sea turtles (Chelonia mydas) in the Republic of Maldives. Photographs were collected ad hoc by marine biologists and citizen scientists around the country from May 2016 to November 2019. Across 10 sites in four atolls, we identified 325 unique hawksbill turtles and 291 unique green turtles-where most were juveniles. Our analyses suggest that, even when controlling for survey effort and detectability dynamics, the populations of both species are stable and/or increasing in the short term at many reefs in the Maldives and the country appears to provide excellent habitat for recruiting juvenile turtles of both species. Our results represent one of the first empirical estimations of sea turtle population trends that account for detectability. This approach provides a cost-effective way for small island states in the Global South to evaluate threats to wildlife while accounting for biases inherent in community science data.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".