Reducing Pest Risk in Birch Wood Products—The Effective Heat Treatment for Bronze Birch Borer <scp><i>Agrilus anxius</i></scp> (Coleoptera: Buprestidae) Prepupae
Bibliographic record
Abstract
ABSTRACT The protection of forest resources and the safe trade of forest products require phytosanitary measures to reduce the risk of pest movement to novel environments. Heat treatment is a widely available, efficient and effective method to produce phytosanitary wood products destined for trade. Defining the minimum effective heat treatment dose required to cause the mortality of insects in wood products reduces the risk of spreading pests to novel environments with the lowest possible energy cost, which in turn reduces environmental impacts and provides confidence in current guidelines for heat treatment regulations. The minimum effective heat treatment dose (time and temperature) for Agrilus anxius (bronze birch borer) prepupae was determined using the Humble water bath applying heat in vitro. Heat treatment was assessed using a controlled increase in temperature to simulate the heat ramp applied to wood in industrial kilns and conventional heat chamber operations. Target temperatures between 51°C and 56°C for exposure durations of 15 and 30 min were tested to determine the minimum effective dose. Prepupal A. anxius did not survive exposure to 53°C, 54°C, 55°C or 56°C for 30 min or 54°C and 56°C for 15 min. Chronic or delayed mortality was observed at 53°C for 15‐min treatments. Evaluating the effect of specific heat treatment parameters for other forest pests is recommended to identify and validate the minimum temperature and time required to cause the mortality of wood pests in order to avoid introducing non‐indigenous species with wood products and limit pest movement.
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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 | 0.000 | 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".