Tree crown ratio models for multi-species and multi-layered stands of southeastern British Columbia
Bibliographic record
Abstract
The ratio of live crown length to tree height (crown ratio; CR) is often used as an important predictor variable for tree level growth equations, particularly for multi-species and multi-layered stands. Also, CR indicates tree vigour and can be an important habitat variable. Measurement of CR for each tree can be time-consuming and difficult to obtain in very dense stands and for very tall trees where the base of live crown is obscured. Models to predict CR from size, competition and site variables were developed for several coniferous and one hardwood tree species growing in multispecies and multi-layered forest stands (complex stands) of southeastern British Columbia. Simple correlations indicated the expected relationships of CR decreasing with increasing height, and with increasing competition. A logistic model form was used to constrain predicted CR values to the interval [0,1]. Also, predictors were divided into tree size, stand competition, and site measures, and the contribution of each set of contributors was examined. For all models, height was an important predictor. The stand competition measure, basal area of larger trees, contributed significantly to predicting CR given that crown competition factor was also included as a measure of competition. Logical trends in CR versus size and competition variable groups were reflected by the models; site variable slightly improved predictions for some species. Much of the variability in CR was not accounted for, indicating that other variables are important for explaining CR changes in these complex stands. Key words: crown ratio, multi-species stands, multi-layered stands, basal area of larger trees
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".