Accounting for error correlations in diameter increment modelling: a case study applied to northern hardwood stands in Quebec, Canada
Bibliographic record
Abstract
In this study, a diameter increment model was calibrated for individual trees in hardwood stands in Quebec, Canada. Two random effects, a first one for the plot and a second one for the time interval nested in the plot, and a covariance structure were included in the model to account for spatial and serial correlations. The diameter at breast height, species group, vigor and product classes, and basal area were the explanatory variables that were tested in this analysis. The adequacy of the covariance features (random effects and covariance structure) were tested through empirical correlations calculated from normalized residuals. A cross-validation was also carried out to evaluate the model. The normalized residuals showed no departure from the assumption of independently and normally distributed error terms with homogeneous variances. Consequently, the statistical inferences could be considered as valid. The results showed that the average diameter increment pattern differs among the species. Although tree product and vigor were significant explanatory variables, their effects were relatively small. On the other hand, basal area had a large and significant negative effect on diameter increment. Our study demonstrates that empirical correlations calculated from normalized residuals can be used as an additional tool to test the adequacy of the covariance features in a mixed-effects model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".