Suitability of five cross validation methods for performance evaluation of nonlinear mixed-effects forest models - a case study
Bibliographic record
Abstract
Five cross validation methods, the k-fold, leave one plot out (LOP), leave one tree per plot out (LOT), 0.632 and 0.632+ bootstrap methods, were examined in this study for their suitability for performance evaluation of seven nonlinear mixed models based on a height–diameter relationship. The k-fold, LOP, 0.632 and 0.632+ methods used plot as the basic unit for data resampling, and applies to situations where predictions are needed for all trees in a new plot not used for model development. All four methods were suitable for evaluating the predictive performance of the selected model(s), and the 0.632 and 0.632+ methods were better than the k-fold and LOP methods. The LOT method used tree as the basic unit for data resampling, and applies to situations where predictions are needed for a portion of trees in a plot not used for model development, while the remaining trees of the plot are used for model development. The LOT method was not suitable for performance evaluation of the selected model(s).
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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 | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".