Density and location of simulated signs of injury affect efficacy of ground surveys for Asian longhorned beetle
Bibliographic record
Abstract
Abstract Surveys for signs of attack by Asian long-horned beetles,Anoplophora glabripennis(Motschulsky) (Coleoptera: Cerambycidae), currently rely upon visual examination of trees to discover signs of attack. By embedding simulatedA. glabripennisoviposition pits and exit holes on open-grown Norway maples,Acer platanoidesL. (Aceraceae), we evaluated the effect of sign density, height (below or above 2.5m), and position (bole or branch) when foliage was present or absent on inspector ability to distinguish trees with or without signs. From this, we quantified detectability, or the proportion of trees correctly identified as infested, and determined the time taken to do so. Effectiveness in detecting trees with signs improved when sign density increased, when signs were below 2.5m, and when oviposition pits were located on boles and exit holes on branches. These main findings require some caveats, due to a number of significant interactions. Foliage presence/absence had no apparent influence on effectiveness; possible reasons are provided for this result. Time-to-find curves, which illustrated the proportion of inspectors who accurately identified an infested tree as a function of survey duration, revealed that for most treatment combinations, most infested trees were detected within the first 2 min of survey time. These findings provide baseline data to assist managers in designing effective protocols for ground surveys ofA. glabripennis.
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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 | 0.001 | 0.003 |
| 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".