DNA barcodes for biosecurity: invasive species identification
Bibliographic record
Abstract
Biosecurity encompasses protecting against any risk through 'biological harm', not least being the economic impact from the spread of pest insects. Molecular diagnostic tools provide valuable support for the rapid and accurate identification of morphologically indistinct alien species. However, these tools currently lack standardization. They are not conducive to adaptation by multiple sectors or countries, or to coping with changing pest priorities. The data presented here identifies DNA barcodes as a very promising opportunity to address this. DNA of tussock moth and fruit fly specimens intercepted at the New Zealand border over the last decade were reanalysed using the cox1 sequence barcode approach. Species identifications were compared with the historical dataset obtained by PCR-RFLP of nuclear rDNA. There was 90 and 96% agreement between the methods for these species, respectively. Improvements included previous tussock moth 'unknowns' being placed to family, genera or species and further resolution within fruit fly species complexes. The analyses highlight several advantages of DNA barcodes, especially their adaptability and predictive value. This approach is a realistic platform on which to build a much more flexible system, with the potential to be adopted globally for the rapid and accurate identification of invasive alien 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".