Modelling of tangential, radial, and longitudinal shrinkage after drying in jack pine and white spruce
Bibliographic record
Abstract
The purpose of this study was to develop an understanding of the pattern of variation in longitudinal, radial, and tangential shrinkage in two commercially important Canadian wood species using multivariate nonlinear mixed-effects models. Shrinkage data were measured in each growth ring at different heights on trees harvested from a mixed-species Nelder tree-spacing plot located in New Brunswick, Canada. Live crown base was reconstructed based on stem analysis. Cambial age, sampling height, crown characteristics (crown length, crown ratio, and distance to live crown base), and ring width at time of wood formation were used as prediction variables. The results indicated that shrinkage magnitude and pattern were closely related to crown dimensions and stem position at time of wood formation. Trees from wider tree spacings with larger crown lengths and ratios tended to have smaller tangential and radial shrinkage. Longitudinal shrinkage decreased nonlinearly from pith outwards with slower rate changes near the bottom of the trees. The model fixed effects accounted for more variation in jack pine (Pinus banksiana Lamb.) than in white spruce (Picea glauca (Moench) Voss) for shrinkage in the same anatomical direction. After further validation, the model developed can be used to provide shrinkage predictions for lumber distortion models within the framework of an individual tree growth model.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".