A simple growth and yield model for assessing changes in standing volume across Canada’s forests
Bibliographic record
Abstract
We have adjusted two growth and yield models to temporary sample plots from across Canada, and used climate variables in lieu of phytometric indices such as site index to represent, in part, the site-level variability in growth potential. Comparison of predicted increments in plot-level height, basal area and merchantable wood volume to increments of these variables measured in permanent sample plots shows a moderate to poor predictive ability. Comparison with the performance of four operational growth and yield models from different provinces across Canada shows comparable predictive power of this new model versus that of the provincial models. Based on these results, we suggest that the simplification of regional growth and yield models may be achieved without further loss of predictive power, and that the large error in the prediction of growth increment is mostly associated with the use of temporary sample plots which, by definition, contain little information on stand dynamics. We also suggest that, because of the empirical nature of these growth and yield models, the scale of application should determine the appropriate scale of the model. National estimates of forest growth are therefore less likely to be biased if obtained from a national model only than if obtained from a combination of regional models, where those exist, gap-filled with estimates from a national model. Key words: yield model, merchantable wood volume, stand age, climatic variables, simultaneous regression, robust regression
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".