Clear wood content in standing trees predicted from branch scar measurements with terrestrial LiDAR and verified with X-ray computed tomography
Bibliographic record
Abstract
Knowledge about the wood quality of standing trees is crucial in that it serves as an excellent means for nearly all stages of the wood-supply chain. Better information about internal wood characteristics can be derived from the outside appearance by establishing a correlation between the bark characteristics of a stem and its internal quality. This paper presents an approach where the quality determination of standing trees using a terrestrial light detection and ranging (LiDAR) system is combined with the information about internal quality of logs using X-ray computed tomography (CT). Results show a high accuracy for branch scar measurements with terrestrial LiDAR and knot measurement with CT. A strong correlation between scar seal quotient and the amount of clear wood could be confirmed using European beech (Fagus sylvatica L.) as an example. Quality grading of virtually segmented logs using terrestrial LiDAR and CT showed moderate correlation; 62.5% of the segments were allocated to the same grade by both approaches. In conclusion, terrestrial LiDAR in forest inventory could be used as an instrument to predict inner wood quality in greater detail by gathering data on the outer appearance and branch scars of standing trees. This additional knowledge has the potential to improve forest planning, bucking instructions, and a roundwood allocation that meets industry demand.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".