Wedding biodiversity inventory of a large and complex Lepidoptera fauna with DNA barcoding
Bibliographic record
Abstract
By facilitating bioliteracy, DNA barcoding has the potential to improve the way the world relates to wild biodiversity. Here we describe the early stages of the use of cox1 barcoding to supplement and strengthen the taxonomic platform underpinning the inventory of thousands of sympatric species of caterpillars in tropical dry forest, cloud forest and rain forest in northwestern Costa Rica. The results show that barcoding a biologically complex biota unambiguously distinguishes among 97% of more than 1000 species of reared Lepidoptera. Those few species whose barcodes overlap are closely related and not confused with other species. Barcoding also has revealed a substantial number of cryptic species among morphologically defined species, associated sexes, and reinforced identification of species that are difficult to distinguish morphologically. For barcoding to achieve its full potential, (i) ability to rapidly and cheaply barcode older museum specimens is urgent, (ii) museums need to address the opportunity and responsibility for housing large numbers of barcode voucher specimens, (iii) substantial resources need be mustered to support the taxonomic side of the partnership with barcoding, and (iv) hand-held field-friendly barcorder must emerge as a mutualism with the taxasphere and the barcoding initiative, in a manner such that its use generates a resource base for the taxonomic process as well as a tool for the user.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".