CHARACTERIZING CHANGBAI LARCH THROUGH VENEERING. PART 1: EFFECT OF STAND DENSITY
Bibliographic record
Abstract
Changbai larch (Larix olgensis Henry) is one of the main plantation species in the northern part of China, but so far its utilization has been limited to solid wood, pulping, and paper products. As part of a national initiative, the objective of this work was to develop a good understanding concerning how stand management practices affect larch wood and veneer properties. To cope with the variation of initial and final stand density, the term "relative thinning intensity (RTI)" was introduced to describe the effect of stand thinning on the resulting wood and veneer properties. Nine representative sample trees each from four stands were harvested, then trimmed and bucked. Six 1.25 m long bolts along the entire tree stem were systematically cut to peel 2.6 mm thick veneers. Clear wood specimens were also sawn from matched bolts for mechanical testing for the purpose of comparison and validation. The correlation between the main tree variables and properties of clear wood and veneer was analyzed, and the influence of RTI on key wood and veneer properties was examined and compared. A good correlation was found in density and modulus of elasticity (MOE) between larch clear wood and veneer, giving an R 2 of 0.75 and 0.71, respectively. This indicates that veneering can be potentially used to characterize this larch species. The stand with the lowest RTI had the shortest rotation but yielded the best properties of both clear wood and veneer, which highlights the importance of stand management in terms of thinning.
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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".