Tomodensitometry as a Tool to Detect and Study Two Agrilus (Coleoptera: Buprestidae) Species
Bibliographic record
Abstract
Exotic insect species are an increasing concern with international trade. Detecting and removing any insect are thus important for any imported/exported product, including wood products. For example, wood transportation is known to be an important pathway for the introduction and dispersal of the Emerald Ash Borer, Agrilus planipennis (Coleoptera: Buprestidae). This Asian species is causing high mortality of ash trees in its introduced range because of the weak natural defense of trees and the virtual absence of natural enemies. For similar reasons, there are concerns in Europe that the Bronze Birch Borer, A. anxius, native to North America, could be introduced and cause important birch mortality. Having efficient detection methods and phytosanitary measures to prevent introducing it is thus important. In this study, we evaluated tomodensitometry—or CT-scan—as a detection method for detecting these two Agrilus spp. using debarking as the method of reference. Using CT-scan, we were also able to precisely measure the depth of insects in ash and birch trees in order to recommend proper phytosanitary measures for exportation and importation of wood products. Both techniques efficiently detect the presence of insects in ash, paper birch, and yellow birch. However, the number of A. anxius detected depended on both the technique and the diameter of the sample. The depth of insects depended on tree species, sample diameter, and life-stage. Globally, A. planipennis are deeper in ash trees than A. anxius in birch trees, and prepupae are deeper than larvae. The maximal depth in the sapwood (excluding bark thickness) for ash, paper birch, and yellow birch was 21.9 mm, 6.30 mm, and 3.22 mm, respectively. While CT-scan is more expensive and requires access to expensive equipment, debarking is more time-consuming, especially if the number of insects needs to be determined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".