Mixed-effects basal area increment models for tree species in the boreal forest of Ontario, Canada using an ecological land classification approach to incorporate site effects
Bibliographic record
Abstract
Individual-tree basal area increment (BAI) models were developed for major tree species in the boreal forest of Ontario, Canada. A composite distance-independent individual-tree BAI model was structured based on the log-linearized gamma base function using a dataset derived from a network of ∼1800 permanent growth plots. A suite of fixed effects covariates that included basal area of trees larger than the subject tree, diameter at breast height, stand basal area and latitude, along with ecological land classification ecosites used as a random effect parameter, were found to be significant covariates in the model. A model fitted with the intercept designated as a mixed-effects parameter had a smaller Akaike's information criterion compared with other competing models. The mixed-effects modelling approach allowed us to select the best suite of covariates by correctly specifying the covariance structure according to the hierarchy of the fitting data. Furthermore, inclusion of ecosite as a random effect parameter increased ecological resolution in the BAI model by localizing its prediction and improves compatibility between growth and yield modelling and other important management tools such as the forest resources inventory.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".