Applications of computed tomography (CT) scanning technology in forest research: a timely update and review
Bibliographic record
Abstract
Organic materials of woody plants are complex and show internal, structural, and morphological variations due to genetic and environmental influences. Variability can be observed in stems, branches, leaves, and roots. Nondestructive and noninvasive technologies have been proposed to assess this variability. Computed tomography (CT) scanning, originally designed for medical diagnostics, permits the measurement of wood properties in situ (e.g., wood density, moisture content, internal defects, annual growth) and crown traits that characterize branching pattern geometry and canopy space occupancy for small-sized trees. Since Wei et al.’s (2011, Can. J. For. Res. 41(11): 2120–2140, doi: 10.1139/x11-111 ) review on the assessment of wood quality for optimized manufacturing processes using a CT scanner, several important developments have occurred, motivating the preparation of an update. We provide technical clarifications about the scales of observation and resolution; report on recent studies in which CT scanning was applied with research objectives beyond wood quality assessment for an optimized manufacturing of forest products; and stress the importance of analytical procedures for the graphical and quantitative analyses of CT scanning data (images and numbers) and the need for specialized algorithms and software. With this review, readers are expected to be well informed of the avenues offered by CT scanning technology in forest research in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".