Development of two reverse transcription‐<scp>PCR</scp> methods to detect living pinewood nematode, <i><scp>B</scp>ursaphelenchus xylophilus</i>, in wood
Bibliographic record
Abstract
Summary Pinewood nematode, Bursaphelenchus xylophilus, is an inhabitant of native pine species of North America, where its presence in trees is non‐pathogenic. By contrast, the introduction of this nematode to forests overseas has devastated some pine stands and is recognized as a pest of phytosanitary concern by some countries' National Plant Protection Organizations. The ability to detect B. xylophilus in internationally traded wood products is crucial to reduce the spread of this organism. Current molecular techniques for the detection of B. xylophilus rely on the presence of genomic DNA and thus will detect both living and dead nematodes without differentiation. The detection of dead nematodes could lead to unnecessary trade disruption. Therefore, accurate techniques for the detection of and differentiation between live and dead B. xylophilus are critical. We have developed an endpoint RT‐PCR assay and a SYBR Green 1 real‐time RT‐PCR assay, both of which selectively identify living pinewood nematode by detecting the presence of Hsp70 mRNA as a viability marker. Both of these assays may help overcome or resolve disputes involving the detection of pinewood nematode at the port of entry and can also be used to evaluate the efficiency of wood treatment procedures.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".