Assessing the wood quality of interior spruce (<i>Picea glauca</i> × <i>P. engelmannii</i>): variation in strength, relative density, microfibril angle, and fiber length
Bibliographic record
Abstract
Abstract A dynamic interrelationship exists among wood density and fiber traits (tracheid length and microfibril angel, MFA) and the ultimate wood strength properties. Moreover, many of the basic fundamental wood attributes are heavily influenced by crown size and architecture. In an attempt to examine this interplay, we thoroughly characterized 60 interior spruce (Picea glauca × P. engelmannii) trees sampled in three age classes from four sites in central British Columbia. Breast height discs were taken, and relative wood density was measured along two radii. Tracheid length was assessed on isolated 5 years increments from pith to bark at breast height for each tree, as was MFA. Segmented regression was used to identify the “juvenile to mature wood” transition point, which revealed transition ages of 9.4 and 15.1 years for wood density and MFA, respectively, while fiber length continued to elongate until near 60 years of age. The flexural properties, modulus of elasticity (MoE) and modulus of rupture (MoR), were also quantified in the 60 individuals and found to be best predicted by V FV, a measure of tree vigor, and not the basic wood attributes. These findings imply that long crowns carrying large amounts of foliage, V FV, negatively impact wood strength in interior spruce.
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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 | 0.001 | 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".