Effect of competition on individual white spruce production in young boreal mixedwood forests
Bibliographic record
Abstract
Increasing the production of wood fibre from conifer species such as white spruce (Picea glauca (Moench) Voss) is one of many challenges in the management of boreal mixedwood forests. The effects of various competition measures on relative growth and relative growth rate variables were calculated for individual white spruce subject trees. Correlation analysis was used to explore relationships with competitor structural features, including the ratio of competitor basal area to subject tree basal area (CBAS), the ratio of competitor height to subject tree height (AHCS), and the proportion of softwood (FSW). Regression analysis was used to explore relationships with three distance-dependent competition indices. The ratio of subject tree height to diameter at breast height (DBH) (HDR), crown ratio (CR), and crown relative increment rate (CRIR) were significantly correlated with CBAS and AHCS. HDR, CR, CRIR, and DBH relative growth rate were all statistically significantly related to the competition indices. Results indicated that (i) relative growth and relative growth rate measures successfully captured a range of competition, (ii) crowns of trees with larger diameters used their horizontal growing space more efficiently to produce stemwood, and (iii) the proportion of softwood contributing to competition did not appear to influence subject tree production efficiency. Growth efficiency variables have the potential to improve our understanding of boreal mixedwood dynamics.
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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 | 0.000 | 0.000 |
| 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".