Aspen competition affects light and white spruce growth across several boreal sites in western Canada
Bibliographic record
Abstract
The effectiveness of competition indices for predicting light transmittance and white spruce ( Picea glauca (Moench) Voss) growth were examined across trembling aspen ( Populus tremuloides Michx.) density gradients using sites from a long-term study of mixedwood growth and development in Alberta and Saskatchewan. Competition indices based on density (number of trees, basal area, and spacing factor), distance-dependent and -independent size ratio (Hegyi’s and Lorimer’s), and crown characteristics (crown volume, surface area, and cross-sectional area) were tested. Transmittance was effectively predicted by crown competition indices followed closely by aspen basal area and size ratio indices. Models of spruce growth indicated better results for stem volume compared with diameter or height. Competition alone accounted for less than 60% of stem growth variation, with basal area and transmittance providing some of the best models. The predictive ability of spruce growth was increased up to 93% by adding initial size as a second explanatory variable. In this respect, initial diameter was superior to initial height, crown volume, and surface area. Relationships between competition, transmittance, and spruce growth were found to differ significantly between geographical locations. These results suggest the need for local development of models relating tree growth to competition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".