Monitoring green turtles (<i>Chelonia mydas</i>) at a coastal foraging area in Baja California, Mexico: multiple indices to describe population status
Bibliographic record
Abstract
From June 1995 to August 2002 we assessed green turtle (Chelonia mydas) population structure and survival, and identified human impacts at Bahía de los Angeles, a large bay that was once the site of the greatest sea turtle harvest rates in the Gulf of California, Mexico. Turtles were captured live with entanglement nets and mortality was quantified through stranding surveys and flipper tag recoveries. A total of 14,820 netting hours (617·5 d) resulted in 255 captures of 200 green turtles. Straight-carapace length and mass ranged from 46·0–100·0 cm (mean=74·3±0·7 cm) and 14·5–145·0 kg (mean=61·5±1·7 kg), respectively. The size–frequency distribution remained stable during all years and among all capture locations. Anthropogenic-derived injuries ranging from missing flippers to boat propeller scars were present in 4% of captured turtles. Remains of 18 turtles were found at dumpsites, nine stranded turtles were encountered in the study area, and flipper tags from seven turtles were recovered. Survival was estimated at 0·58 for juveniles and 0·97 for adults using a joint live-recapture and dead-recovery model (Burnham model). Low survival among juveniles, declining annual catch per unit effort, and the presence of butchered carcasses indicated human activities continue to impact green turtles at this foraging area.
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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 | 0.000 |
| 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) | 0.001 | 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".