Percent stocking models for four major Alberta tree species
Bibliographic record
Abstract
Based on stem-mapped permanent sample plot data, percent stocking dynamics were modelled for four major tree species in the province of Alberta, Canada: trembling aspen (Populus tremuloides Michx.), white spruce (Picea glauca (Moench) Voss), lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) and black spruce (Picea mariana (Mill.) B.S.P.). Percent stocking was defined as the percentage of 10-m2 plots occupied by at least one tree with a minimum height of 1.3 m. The difference equation technique was applied for model fitting. Site index was a significant predictor in all models. Heterogeneous residual variances present in the aspen, white spruce and lodgepole pine models were corrected by applying a power-of-the-mean variance function. Serial correlation due to repeated measurements was considered through a linear log covariance structure. A percent stocking index, defined as the percent stocking at 50 years total age, was derived from the fitted percent stocking models and used for assessing species interactions in mixed-species stands. The presence of other species did not appear to affect percent stocking changes over time for aspen and black spruce. However, white spruce percent stocking was reduced when growing together with aspen, and lodgepole pine percent stocking was negatively affected by the presence of aspen, white spruce and black spruce.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".