Using Nonlinear Mixed Model Technique to Determine the Optimal Tree Height Prediction Model for Black Spruce
Bibliographic record
Abstract
Based on the nonlinear mixed model technique, four base height-diameter models were evaluated for black spruce. The Chapman-Richard model was chosen. Top height and basal area were incorporated into the base model. Comparison of the base and expanded models showed that, although the goodness-of-fit measures on the modelling data were improved with the inclusion of top height and basal area, the predictive accuracy of the expanded models at the subject-specific level where the predominant interest of nonlinear mixed models lies, was reduced when tested on the model validation data. This has important practical implications because more accurate individual tree height predictions can be better achieved using the base height-diameter model without requiring the addition of other variables. It also reaffirms that determining the adequacy of a model on model fitting statistics alone can be misleading. A fitted model is best judged on separate validation data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".