A DNA barcode survey of insect biodiversity in Pakistan
Bibliographic record
Abstract
Although Pakistan has rich biodiversity, many groups are poorly known, particularly insects. To address this gap, we employed DNA barcoding to survey its insect diversity. Specimens obtained through diverse collecting methods at 1,858 sites across Pakistan from 2010-2019 were examined for sequence variation in the 658 bp barcode region of the cytochrome c oxidase 1 (COI) gene. Sequences from nearly 49,000 specimens were assigned to 6,590 Barcode Index Numbers (BINs), a proxy for species, and most (88%) also possessed a representative image on the Barcode of Life Data System (BOLD). By coupling morphological inspections with barcode matches on BOLD, every BIN was assigned to an order (19) and most (99.8%) were placed to a family (362). However, just 40% of the BINs were assigned to a genus (1,375) and 21% to a species (1,364). Five orders (Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera) accounted for 92% of the specimens and BINs. More than half of the BINs (59%) are so far only known from Pakistan, but others have also been reported from Bangladesh (13%), India (12%), and China (8%). Representing the first DNA barcode survey of the insect fauna in any South Asian country, this study provides the foundation for a complete inventory of the insect fauna in Pakistan while also contributing to the global DNA barcode reference library.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".