DNA barcodes as a tool in biodiversity research: testing pre-existing taxonomic hypotheses in Delphic Apollo butterflies (Lepidoptera, Papilionidae)
Bibliographic record
Abstract
Numerous studies have demonstrated that DNA barcoding is an effective tool for detecting DNA clusters, which can be viewed as operational taxonomic units (OTUs), useful for biodiversity research. Frequently, the OTUs in these studies remained unnamed, not connected with pre-existing taxonomic hypotheses, and thus did not really contribute to feasible estimation of species number and adjustment of species boundaries. For the majority of organisms, taxonomy is very complicated with numerous, often contradictory interpretations of the same characters, which may result in several competing checklists using different specific and subspecific names to describe the same sets of populations. The highly species-rich genus Parnassius (Lepidoptera: Papilionidae) is but one example, such as several mutually exclusive taxonomic systems have been suggested to describe the phenotypic diversity found among its populations. Here we provide an explicit flow chart describing how the DNA barcodes can be combined with the existing knowledge of morphology-based taxonomy and geography (sympatry versus allopatry) of the studied populations in order to support, reject or modify the pre-existing taxonomic hypotheses. We then apply this flow chart to reorganize the taxa within the Parnassius delphius species group, solving long-standing taxonomic problems.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".