Variations in ring width density and tracheid morphology of tamarack wood (<i>Larix laricina</i>)
Bibliographic record
Abstract
The tree genetic improvement programs focus on tree growth with little attention to wood quality despite determining the potential of wood for various applications. This study investigated the intra-ring and intra-tree variations of wood growth, density, tracheid length, and width of Larix laricina trees and estimated their quantitative genetic parameters of a 30-year-old progeny test using destructive and nondestructive samplings. The average ring density was 491 kg/m3. The proportion of latewood remains uniform and constant within the tree at about 24%. The tracheids were fine and long, averaging 25 µm in diameter and 2.23 mm in length for earlywood and 25 µm in diameter and 2.55 mm in length for latewood. The cambial age has a significant effect on almost all wood properties. A positive and significant phenotypic and genotypic correlation between density components was found for juvenile and mature wood. Tracheid morphological properties were positively correlated with each other and negatively correlated with wood density and growth components, except for earlywood density. Heritability estimates indicate that wood density components were under moderate to strong genetic control. These results showed that wood quality traits are important selection criteria for breeding programs to improve wood quality while maintaining a high growth rate.
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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 | 0.000 |
| 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) | 0.001 | 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".