Development of a tree-specific stem profile model for white spruce: a nonlinear mixed model approach with a generalized covariance structure
Bibliographic record
Abstract
A variable-exponent stem profile model was developed for white spruce (Picea glauca (Moench) Voss) trees in Alberta using a nonlinear mixed model approach. Between-tree variation in stem diameter was best captured by incorporating three random parameters into the model. Heterogeneous residual variances were modelled as a power function of tree breast height diameter. Within-tree residual autocorrelation was modelled through a covariance structure. Based on model fitting statistics, the four-banded toeplitz (TOEP(4)) and the spatial power structures were both selected for further evaluation. An independent validation dataset was used for evaluating the calibration accuracy of the final model in stem diameter and volume predictions. To make tree-specific calibrations, one or more prior diameter measures should be available from each tree. For this study, four scenarios were evaluated, in which one, two, three and four prior diameters were randomly selected for predicting random parameters by an approximate Bayesian estimator. Tree-specific calibrations were subsequently derived. Population-level predictions were also produced for comparison. The TOEP(4) structure was better in general than the spatial power structure for predicting stem diameter and total tree and section volumes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".