USING COMPUTED TOMOGRAPHY SCANNING TECHNOLOGY TO EXTRACT VIRTUAL WOOD CORES, DERIVE WOOD DENSITY RADIAL PATTERNS, AND TEST HYPOTHESIS ABOUT DIRECTION, CORE SIZE, AND YEAR OF GROWTH
Bibliographic record
Abstract
Computed tomography (CT) scanning technology was used to collect millions of three-dimensional data called “CT numbers”, on two sets of white spruce (Picea glauca [Moench] Voss) wood disks. Data collected were then converted to wood density estimates using a calibration equation for wood. Virtual wood cores with three different sizes (i.e., 1 voxel – the smallest volumetric unit on which a CT number was computed, 5 mm and 12 mm in diameter) were extracted from pith to bark and in four orthogonal directions. This made it possible to test the effects of core direction and size on the wood density estimates obtained. The average values as well as the radial patterns of wood density as estimated from CT scanning data were found to be typical of the values and patterns reported for the white spruce tree species in the literature, especially in relation to cambial age as the experimental trees were of different ages. In conclusion, wood science application of CT scanning technology allows extracting data subsets in 3D to perform density estimation, pattern analysis and hypothesis testing, and the results are valuable complements to those obtained with other technologies such as X-ray densitometry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".