Assessing the goodness of fit of forest models estimated by nonlinear mixed-model methods
Bibliographic record
Abstract
In this study we examined various measures, including the concordance correlation (CC) coefficient, for determining the goodness of fit of forest models estimated by nonlinear mixed-model (NLMM) methods. Based on the volume–age data for black spruce, we analyzed the use of CC and other traditional goodness-of-fit measures such as coefficient of determination (R 2 ), mean bias, percent bias, root mean square error, and graphic techniques on both the population and subject-specific levels within the NLMM framework. We also examined the relationship between goodness-of-fit measures and the number of observations per subject. We found that the standard overall goodness-of-fit measures commonly reported on combined data from different subjects were generally insufficient in determining the goodness of fitted models. We recommend that CC and other selected goodness-of-fit measures be calculated for individual subjects, and that the frequency distributions of the calculated values be examined and used as the principal criteria for determining the goodness of fit of forest models estimated by NLMM methods and for comparing alternative models and covariance structures. We also emphasized the importance of using pertinent graphic techniques to assess the appropriateness of NLMMs, especially at the subject-specific level, wherein lies the main interest of NLMMs.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".