Field calibration of blowfly-derived DNA against traditional methods for assessing mammal diversity in tropical forests
Bibliographic record
Abstract
Mammal diversity assessments based on DNA derived from invertebrates have been suggested as alternatives to assessments based on traditional methods; however, no study has field-tested both approaches simultaneously. In Peninsular Malaysia, we calibrated the performance of mammal DNA derived from blowflies (Diptera: Calliphoridae) against traditional methods used to detect species. We first compared five methods (cage trapping, mist netting, hair trapping, scat collection, and blowfly-derived DNA) in a forest reserve with no recent reports of megafauna. Blowfly-derived DNA and mist netting detected the joint highest number of species (n = 6). Only one species was detected by multiple methods. Compared to the other methods, blowfly-derived DNA detected both volant and non-volant species. In another forest reserve, rich in megafauna, we calibrated blowfly-derived DNA against camera traps. Blowfly-derived DNA detected more species (n = 11) than camera traps (n = 9), with only one species detected by both methods. The rarefaction curve indicated that blowfly-derived DNA would continue to detect more species with greater sampling effort. With further calibration, blowfly-derived DNA may join the list of traditional field methods. Areas for further investigation include blowfly feeding and dispersal biology, primer biases, and the assembly of a comprehensive and taxonomically-consistent 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".