In situ molecular detection of some white-rot and brown-rot basidiomycetes infecting temperate and tropical woods
Bibliographic record
Abstract
Wood-decay white-rot and brown-rot fungi have a major economic impact on commercial and manufactured tropical and temperate woods. The aim of this study was to design a molecular method, coupled with polymerase chain reaction (PCR) and DNA sequencing, to enable early identification of various forms of fungal decay in various types of wood. The resulting tool could be used to certify the healthiness of commercial woods and also to make more efficient use of chemicals and thus reduce their negative environmental impact. Sapwood plates of Distemonanthus benthamianus,Fagus sylvatica, Lophira alata, Pinus sylvestris, and Pycnanthus angolensis were incubated in vitro in the presence of Fibroporia vaillantii, Coniophora puteana, Gloeophyllum trabeum, Pycnoporus sanguineus, and Trametes versicolor according to the EN 113 standard method. Average mass losses ranging from 2.6% to 25.0% indicated that all wood samples had been actually infected and enabled us to test the reliability of our method. PCR products were obtained in 24 of 25 combinations, and DNA sequences were obtained in 21 of the 24 fungal PCR products. DNA sequences obtained from infected wood were compared with sequences from pure strains, thus confirming the identity of the infecting strains with 100% similarity for an average of 412 bp.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".