A duplex ddPCR assay for simultaneously detecting <i>Ips sexdentatus</i> and <i>Ips typographus</i> (Coleoptera: Curculionidae) in bulk trap samples
Bibliographic record
Abstract
Bark beetles in the family Curculionidae present a growing hazard to forests worldwide. Like native bark beetles, introduced exotic species can pose a serious threat to North American forests. Ips typographus (Boerner) and Ips sexdentatus (Linnaeus), both native to Europe, are two such pests that have caused widespread forest loss in their native ranges. International trade has led to increased interceptions of Scolytine beetles at ports of entry to the United States. Most intercepted individuals are not identified to species due to lack of expert identifiers, poor specimen quality, or incomplete taxonomy. These same problems affect identification for domestic surveys. Therefore, development of molecular methods for identification of potentially invasive Ips species is essential. Because of the need to scrutinize large numbers of beetles in an efficient manner, we describe a duplex droplet digital PCR (ddPCR) assay to identify I. typographus and I. sexdentatus simultaneously in bulk trap samples containing 500 Scolytinae specimens using a scalable, two-step DNA extraction. This ddPCR method is highly effective for processing the entire contents of beetle traps and identifying these potentially invasive species in a timely and definitive manner. We also describe a nondestructive DNA extraction technique that preserves specimens for morphological identification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".