Ecosite-based predictive modeling of black spruce (<i>Picea mariana</i>) wood quality attributes in boreal Ontario
Bibliographic record
Abstract
Enhanced forest resources inventory systems delineate and define polygons based on fundamental ecological units such as ecosites, which are standard combinations of vegetation and substrate types. Our study objective was to model wood quality characteristics of individual black spruce (Picea mariana (Mill.) B.S.P.) trees across a representative boreal forest landscape in northeastern Ontario, Canada, based on relationships to ecosite and other stand-level variables. A total of 127 large (12 mm) increment core samples were extracted at breast height from dominant or co-dominant black spruce trees in forest stands representing a gradient from dry sandy to wet mineral and organic ecosites. Sample cores were prepared, processed, and analyzed using standard SilviScan protocols. Hierarchical classification models were then fitted using Random Forests to predict density and latewood percentage for black spruce stems at a reference age of 50 years. These models each explained over 32% of variance, with estimated root mean squared errors of 40.4 kg·m−3 and 5.6% for density and latewood percentage, respectively. Among tree-, site-, and stand-level covariates, ecosite group was the most important predictive variable. Knowledge of ecosite – wood quality relationships could support efficient planning for black spruce management by including an indication of potential use as a modeled variable in a forest inventory system.
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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 | 0.001 |
| 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 | 0.001 | 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".