Influence of Tree, Stand, and Site Attributes on Hardwood Product Yield: Insights into the Acadian Forests
Bibliographic record
Abstract
Acadian forests of New Brunswick, Canada, are highly variable both in terms of species composition and quality. This is particularly accurate for hardwoods, for which value recovery is closely influenced by wood quality and a proper understanding of tree attributes. Therefore, based on several databases created between 2012 and 2021, the objectives of this study were to (1) characterize the stand-level distribution of species, size, form, and risk according to site factors and (2) determine the influence of different tree, stand, and site factors on the recovery of merchantable, sawlog, and veneer volumes. In total, 287,984 trees stemming from 9233 plots were analyzed for objective 1. For a subset of trees (743), tree, stand, and site attributes were also related to the product output of harvesting operations through a comprehensive inventory of standing trees and their associated products. Analyses were performed with linear and multinomial logistic regressions as well as factor analysis of mixed data (FAMD). Key results indicated that tree attributes and product recovery were significantly influenced by (1) environmental factors (soil, temperature, and precipitation), summarized by ecoregion in this study and (2) tree size and form. These findings suggest that the inclusion of site factors in supply planning could improve product recovery during forest operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".