Comparing and combining distance‐based and character‐based approaches for barcoding turtles
Bibliographic record
Abstract
Molecular barcoding can serve as a powerful tool in wildlife forensics and may prove to be a vital aid in conserving organisms that are threatened by illegal wildlife trade, such as turtles (Order Testudines). We produced cytochrome oxidase subunit one (COI) sequences (650 bp) for 174 turtle species and combined these with publicly available sequences for 50 species to produce a data set representative of the breadth of the order. Variability within the barcode region was assessed, and the utility of both distance-based and character-based methods for species identification was evaluated. For species in which genetic material from more than one individual was available (n = 69), intraspecific divergences were 1.3% on average, although divergences greater than the customary 2% barcode threshold occurred within 15 species. High intraspecific divergences could indicate species with a high degree of internal genetic structure or possibly even cryptic species, although introgression is also probable in some of these taxa. Divergences between species of the same genus were 6.4% on average; however, 49 species were <2% divergent from congeners. Low levels of interspecific divergence could be caused by recent evolutionary radiations coupled with the low rates of mtDNA evolution previously observed in turtles. Complementing distance-based barcoding with character-based methods for identifying diagnostic sets of nucleotides provided better resolution in several cases where distance-based methods failed to distinguish species. An online identification engine was created to provide character-based identifications. This study constitutes the first comprehensive barcoding effort for this seriously threatened order.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".