DNA barcoding the Lepidoptera inventory of a large complex tropical conserved wildland, Area de Conservacion Guanacaste, northwestern Costa Rica
Bibliographic record
Abstract
The 37-year ongoing inventory of the estimated 15 000 species of Lepidoptera living in the 125 000 terrestrial hectares of Area de Conservacion Guanacaste, northwestern Costa Rica, has DNA barcode documented 11 000+ species, and the simultaneous inventory of at least 6000+ species of wild-caught caterpillars, plus 2700+ species of parasitoids. The inventory began with Victorian methodologies and species-level perceptions, but it was transformed in 2004 by the full application of DNA barcoding for specimen identification and species discovery. This tropical inventory of an extraordinarily species-rich and complex multidimensional trophic web has relied upon the sequencing services provided by the Canadian Centre for DNA Barcoding, and the informatics support from BOLD, the Barcode of Life Data Systems, major tools developed by the Centre for Biodiversity Genomics at the Biodiversity Institute of Ontario, and available to all through couriers and the internet. As biodiversity information flows from these many thousands of undescribed and often look-alike species through their transformations to usable product, we see that DNA barcoding, firmly married to our centuries-old morphology-, ecology-, microgeography-, and behavior-based ways of taxonomizing the wild world, has made possible what was impossible before 2004. We can now work with all the species that we find, as recognizable species-level units of biology. In this essay, we touch on some of the details of the mechanics of actually using DNA barcoding in an inventory.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".