Evidence to support phytosanitary policies–the minimum effective heat treatment parameters for pathogens associated with forest products
Bibliographic record
Abstract
Research on reducing the movement of pests on wood products has led to several options for safer trade including heat treatment of wood to mitigate pests. In this study, pathogenic organisms commonly regulated in the trade of forest products were tested to determine the minimum heat dose (temperature and time) required to cause mortality. The mycelial stage of tree pathogens, Heterobasidion occidentale, Grosmannia clavigera, Bretziella fagacearum, Phytophthora cinnamomi, P. lateralis, P. ramorum and P. xmultiformis, which may be found in untreated wood products, were tested in vitro using the Humble water bath with parameters simulating the rate of heat applied to wood in a commercial kiln. RNA detection using reverse transcription real-time PCR was used to validate pathogen mortality following treatment for: P. ramorum, P. lateralis, P. cinnamomi, P. xmultiformis and G. clavigera. The lethal temperature for all pathogens ranged from 44 to 50°C for a 30-min treatment duration. Using this method to evaluate heat treatment for other forest product pests is recommended to accurately identify the minimum dose required to support phytosanitary trade. With more data potentially lower heat treatment applications may be recommended under specific conditions to produce more efficient and economical heat treatment schedules and reduce environmental impacts.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".