Climate, location, and growth relationships with wood stiffness at the site, tree, and ring levels in white spruce (<i>Picea glauca</i>) in the Boreal Plains ecozone
Bibliographic record
Abstract
We examined modulus of elasticity (MoE) derived from SilviScan in white spruce (Picea glauca (Moench) Voss) at the site, tree, and ring levels across the Boreal Plains ecozone in Canada. Area-weighted averages of MoE were calculated for juvenile and mature wood and were examined in relation to radial growth, climate, and location. Correlations indicated that there was a negative relationship between radial growth and MoE in the juvenile wood that was detectable at the site and tree levels; however, the relationship weakened in the mature wood, particularly at the site level. Few climate variables were correlated with MoE in juvenile wood, whereas multiple summer and fall climate variables showed a significant correlation with MOE in mature wood. A model describing the radial profile of MoE explained 58% of the variability in MoE, with 10% of the random variability attributed to between-tree differences. Elevation and summer water balance accounted for nearly all of the between-site variability. A decrease in MoE with increasing elevation was noted and has been previously linked to a decrease in cell wall thickness due to a shorter growing season at higher elevations. Integration of the MoE profile model into an individual-tree growth and yield simulator is the next logical step.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".