Detection of<i>Ganoderma australe</i>Decay in Three<i>Acacia confusa</i>Trees: A Case Study
Bibliographic record
Abstract
Abstract Background This study aims to utilize various nondestructive methods to assess the internal trunks of 3Acacia confusatrees affected byGanoderma australedecay. Methods Visual Tree Assessment (VTA) was employed to examine the trees, selecting 3 Taiwan acacia (Acacia confusa) trees at the base of whichG. australefungal fruiting bodies were growing, identified as severely damaged and classified as having an immediate hazard level. Subsequently, a stress wave device was used to detect the cross-sectional area of these trees at the locations whereG. australefungus was growing in order to obtain 2D tomographies of stress wave velocity. Following this, a resistance drilling instrument was used to examine the same cross-sectional areas, acquiring resistance drilling amplitude data. Finally, the 3 trees were felled, and 15-cm thick discs were cut from the same cross-sectional areas for laboratory testing. Results Using 2D sonic tomography and a corresponding velocity grid map of stress wave velocity revealed areas with varying velocities across the trunk cross sections. Drill resistance profile curves depicted changes in resistance strength, while visual inspections of disk cross sections indicated the location and severity of decay. Additionally, pilodyn penetration testing showed different penetration depths on the surfaces of the disk cross sections. Conclusion The study discusses the use of these detective methods to discover the location and extent of decay within tree trunks and assesses the percentage of decay in cross-sectional areas, providing a reference for tree risk assessment levels.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".