Mapping global biodiversity connections with DNA barcodes: Lepidoptera of Pakistan
Bibliographic record
Abstract
Sequences from the DNA barcode region of the mitochondrial COI gene are an effective tool for specimen identification and for the discovery of new species. The Barcode of Life Data Systems (BOLD) (www.boldsystems.org) currently hosts 4.5 million records from animals which have been assigned to more than 490,000 different Barcode Index Numbers (BINs), which serve as a proxy for species. Because a fourth of these BINs derive from Lepidoptera, BOLD has a strong capability to both identify specimens in this order and to support studies of faunal overlap. DNA barcode sequences were obtained from 4503 moths from 329 sites across Pakistan, specimens that represented 981 BINs from 52 families. Among 379 species with a Linnaean name assignment, all were represented by a single BIN excepting five species that showed a BIN split. Less than half (44%) of the 981 BINs had counterparts in other countries; the remaining BINs were unique to Pakistan. Another 218 BINs of Lepidoptera from Pakistan were coupled with the 981 from this study before being compared with all 116,768 BINs for this order. As expected, faunal overlap was highest with India (21%), Sri Lanka (21%), United Arab Emirates (20%) and with other Asian nations (2.1%), but it was very low with other continents including Africa (0.6%), Europe (1.3%), Australia (0.6%), Oceania (1.0%), North America (0.1%), and South America (0.1%). This study indicates the way in which DNA barcoding facilitates measures of faunal overlap even when taxa have not been assigned to a Linnean species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".