Detection and quantification of <i>Leptographium wageneri</i>, the cause of black-stain root disease, from bark beetles (Coleoptera: Scolytidae) in Northern California using regular and real-time PCR
Bibliographic record
Abstract
Black-stain root disease is a threat to conifer forests in western North America. The disease is caused by the ophiostomatoid fungus Leptographium wageneri (W.B. Kendr.) M.J. Wingf., which is associated with a number of bark beetle (Coleoptera: Scolytidae) and weevil species (Coleoptera: Curculionidae). We developed a polymerase chain reaction test to identify and quantify fungal DNA directly from insects. Leptographium wageneri DNA was detected on 142 of 384 bark beetle samples (37%) collected in Lassen National Forest, in northeastern California, during the years 2001 and 2002. Hylastes macer (LeConte) was the bark beetle species from which Leptographium DNA was amplified most regularly (2001: 63.4%, 2002: 75.0% of samples). Lower insectfungus association rates were found for Hylurgops porosus (LeConte), Hylurgops subcostulatus (Mannerheim), Hylastes gracilis (LeConte), Hylastes longicollis (Swaine), Dendroctonus valens (LeConte), and Ips pini (Say). The spore load per beetle ranged from 0 to over 1 × 105 spores, with only a few beetles carrying more than 1 × 103 spores. The technique permits the processing of a large number of samples synchronously, as required for epidemiological studies, to study infection rates in bark beetle populations and to identify potential insect vectors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".