Modelling wood density and modulus of elasticity in white spruce plantations in Eastern Québec
Bibliographic record
Abstract
Forest managers have to take into account multiple objectives such as stand yield, wood quality attributes, ecological constraints and social considerations when making their decisions. The objective of the present study is to build (i) a dynamicmodulus of elasticity (MOEdyn) model and (ii) a core wood density (WDcore) model for white spruce plantations in theBas-Saint-Laurent Region (Québec, Canada) to quantify their inter- and intra-stand variations in order for managers tobetter weigh their different options. Using data obtained from 54 sample plots in 31 white spruce plantations from Eastern Québec, the MOEdynof 143 trees and the WDcoreof 162 trees were analysed. Dendrometric and stand variables wereused to build a MOEdynlinear mixed-effect model and a WD multiple linear regression model. The MOEdynmodel explained 66.8% of the total variation, 1.1% of which originated from inter-stand variations. MOEdynwas proportionalto diameter at breast height (DBH) and non-linearly decreased with tree growth rate. The WDcoremodel explained 16.0%of the total variation. The intra-stand variations were represented by a negative relationship between WDcoreand growthrate. Inter-stand variations were accounted for by site index and altitude. The performance of the MOEdynmodel was satisfactory and in accordance with the literature. However, the WDcoremodel was below standard, mainly as a consequenceof unaccounted intra-individual variations. Nonetheless, raw simulations using these models suggest that white sprucewood from plantations may benefit from intensive forest management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".