Systematic Resource Characterization Through Veneering and Nondestructive Testing
Bibliographic record
Abstract
In this study, a systematic approach was established for resource characterization via veneering and nondestructive testing. A recent study with short-rotation western hemlock ( Tsuga heterophylla [Raf.] Sarg) and amabilis fir ( Abies amabilis [Dougl.] Forbes) in British Columbia, Canada, was showcased to demonstrate the effectiveness of this approach. By proper tree sampling, veneer processing, and nondestructive testing on a sheet basis, the proposed approach helps rapidly address several critical issues on resource characterization and utilization, such as 1) the impact of stand characteristics on wood properties including density and modulus of elasticity (MOE) or attributes such as wood moisture content and color; 2) the within-tree and between-tree variations of these wood properties or attributes; 3) the spatial distribution of log defects, such as knots and decay; 4) the effect of tree growth rate, stem position, juvenile and mature wood, sapwood, and heartwood on key veneer properties such as thickness, surface roughness, density and MOE; and 5) veneer yield, visual grade, stress grade, and high-value product potentials. To maximize the value return from the available resource, this approach involves an assessment for product options with predicted grade outturns.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".