Application of electrical resistance tomograph (ERT): innovative non-destructive method in analysing interiors of standing trees in tropics
Bibliographic record
Abstract
The electrical resistance tomograph (ERT) is a customized tree specific novel technology that was developed to monitor and estimate the tree growth and development by looking into the inner structure of the standing tree to analyse the growth/heartwood and health/decay status. Electrical resistivity tomography (ERT) is a valuable tool in tropical forestry for assessing the heartwood-sapwood boundary and detecting wood decay. By measuring electrical resistivity at multiple points around a tree trunk, ERT creates detailed profiles of the tree's internal structure, identifying variations in moisture content, density, and decay. This non-invasive method provides accurate, real-time data that aid in sustainable forest management, conservation, and logging decisions. It allows for precise identification of heartwood, sapwood, and decayed areas without harming the tree, making it a cost-effective and eco-friendly approach for monitoring tree health. This paper is addressing the possibility of exploring the application of ERT on economically important tropical trees viz. sandalwood ( Santalum album L.), red sanders ( Pterocarpus santalinus L.f.), and agarwood ( Aquilaria agalocha Roxb.) to know the presence/absence of heartwood/decay in standing trees and, also, to know presence/absence of decay and extent of decay in standing trees using ERT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".