DNA barcode analysis of the endangered green turtle (<i>Chelonia mydas</i>) in Mexico<sup>1</sup>
Bibliographic record
Abstract
) are realized through molecular approaches including DNA barcoding. We characterized the usefulness of COI DNA barcodes in green turtles in Mexico to better understand genetic divergence and other genetic parameters of this species. We analyzed 63 sequences, including 25 from green turtle field specimens collected from the Gulf of Mexico and from the Mexican Pacific and 38 already present in the Barcode of Life Data Systems (BOLD). A total of 13 haplotypes were identified with four novel haplotypes from the Pacific Ocean and three novel haplotypes from the Atlantic Ocean. Intraspecific distance values among COI gene sequences by two different models were 0.01, demonstrating that there is not a subdivision for green turtle species. Otherwise, the interspecific distance interval ranged from 0.07 to 0.13, supporting a clear subdivision among all sea turtle species. Haplotype and total nucleotide diversity values of the COI gene reflect a medium genetic diversity average. Green turtles of the Mexican Pacific showed common haplotypes to some Australian and Chinese turtles, but different from the haplotypes of the Mexican Atlantic. COI analysis revealed new haplotypes and confirmed that DNA barcodes were useful for evaluation of the population diversity of green turtles in Mexico.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".