Barcoding utility in a mega-diverse, cross-continental genus: keeping pace with Cyrtodactylus geckos
Bibliographic record
Abstract
Over the past decade, DNA barcoding has become a staple of low-cost molecular systematic investigations. The availability of universal primers and subsidized sequencing projects (PolarBOL, SharkBOL, SpongeBOL) have driven this popularity, often without appropriate investigation into the utility of barcoding data for the taxonomic group of interest. Here, our primary aim is to determine the phylogenetic value of DNA barcoding (mitochondrial locus COI) within the gecko genus Cyrtodactylus. With >40 new species described since last systematic investigation, Cyrtodactylus represents one of the most diverse extant squamate genera, and their contemporary distribution spans the Indian subcontinent, eastward through Indochina, and into AustraloPapua. The complex biogeographic history of this group, and morphology-only designation of many species have complicated our phylogenetic understanding of Cyrtodactylus. To highlight the need for continued inclusive molecular assessment, we use Vietnamese Cyrtodactylus as a case study showing the geopolitically paraphyletic nature of their history. We compare COI to the legacy marker ND2, and discuss the value of COI as an interspecific marker, as well as its shortcomings at deeper evolutionary scales. We draw attention back to the Cold Code as a subsidized method for incorporating molecular methods into species descriptions in the effort to maintain accurate phylogenies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 0.001 |
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".