Comparative plasma biochemistry analyte data in nesting leatherback (<i>Dermochelys coriacea</i>), foraging green (<i>Chelonia mydas</i>) and foraging and nesting hawksbill (<i>Eretmochelys imbricata</i>) sea turtles in Grenada, West Indies
Bibliographic record
Abstract
Abstract Blood biochemistry represents a minimally invasive tool for monitoring sea turtle health, assessing injured sea turtles and supporting conservation strategies. In Grenada, West Indies, plasma biochemical variables were examined in 33 nesting leatherback (Dermochelys coriacea), 49 foraging green (Chelonia mydas), 49 foraging hawksbill (Eretmochelys imbricata) and 12 nesting hawksbill sea turtles sampled between 2017 and 2022. Plasma biochemistry reference intervals are described herein except for nesting hawksbills, which are represented by descriptive statistics due to the low sample size. Select analyte concentrations were positively correlated with curved carapace length in leatherbacks (chloride), green turtles (total protein, albumin and globulin) and foraging hawksbills (total protein, albumin and phosphorus). Cholesterol (7.8 mmol/l ± 1.6 SD) and triglyceride (6.9 mmol/l ± 1.9 SD) concentrations were significantly higher in leatherbacks compared to foraging green turtles, foraging hawksbills and nesting hawksbills (P < 0.001 for all). Cholesterol was significantly higher in nesting hawksbills compared to foraging green turtles (P = 0.050) and foraging hawksbills (P = 0.050). Foraging hawksbills demonstrated significantly higher aspartate transaminase activities than leatherbacks (P = 0.002), green turtles (P = 0.009) and nesting hawksbills (P < 0.001). Biochemical results provide baseline population health data and support guidance for treatments during clinical sea turtle rehabilitation efforts. They also provide insight into species-specific physiologic differences and preludes further studies to better characterize the impacts of life-stage class on biochemistry reference intervals to better support wild sea turtle populations in Grenada.
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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 | 0.000 |
| 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".