Seeking alternatives to probit 9 when developing treatments for wood packaging materials under ISPM No. 15
Bibliographic record
Abstract
ISPM No. 15 presents guidelines for treating wood packaging material used in international trade. There are currently two approved phytosanitary treatments: heat treatment and methyl bromide fumigation. New treatments are under development, and are needed given that methyl bromide is being phased out. Probit 9 efficacy (100% mortality of at least 93 613 test organisms) has been suggested as an evaluation criterion for new wood treatments, and is based on fruit fly research. We question requiring probit 9 efficacy for wood pests (insects, nematodes and fungi) and discuss challenges to meeting this requirement. Instead, we suggest a 3‐step, laboratory‐based alternative approach. Step 1 involves laboratory experiments (screening) to estimate the lethal dose for the most tolerant stage of each target pest. We consider each infested piece of wood as an experimental unit, not the individual pests, to avoid pseudoreplication. Step 2 requires replicated experiments (with no survivors) at the estimated lethal dose. We suggest a minimum sample size of 60 experimental units, which achieves 0.95 statistical reliability at the 95% confidence level. Step 3 entails studies under simulated operational conditions using wood samples similar in size to wood packaging material and infested to levels that reflect field conditions.
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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 | 0.059 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".