A multilevel individual tree basal area increment model for aspen in boreal mixedwood stands
Bibliographic record
Abstract
Based on a multilevel nonlinear mixed model approach, a basal area increment model was developed for individual aspen ( Populus tremuloides Michx.) trees growing in boreal mixedwood stands in Alberta. Various stand and tree characteristics were evaluated for their contributions to model improvement. Total stand basal area, basal area of larger trees, and the ratio of target tree height to maximum stand height were found to be significant predictors. When random effects were modeled at the plot level alone, correlations among normalized residuals remained significant. These correlations were successfully removed when random effects were modeled at both plot and tree levels. The predictive abilities of two alternative models were evaluated at the population, plot, and tree levels. At the tree level, a tree measured at the first growth period was used for estimating random parameters, and basal area increments of that tree in future growth periods were subsequently predicted. At the plot level, one to five trees in each plot at each growth period were used to estimate random parameters. Basal area increments of the remaining trees in the same plot at the same growth period were subsequently predicted. The final model provided accurate predictions at all three levels.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".