Modeling response curves and testing treatment effects in repeated measures experiments: a multilevel nonlinear mixed-effects model approach
Bibliographic record
Abstract
A multilevel nonlinear mixed-effects modeling approach is used to model loblolly pine (Pinus taeda L.) stand volume growth in conjunction with four silvicultural treatments. Comparisons of treatment effects over time are integrated with the model-building process. Three-level random effects are introduced into a modified Richards growth model. Within-plot heterogeneity and correlation still occur, which are described by the exponential variance function and a first-order autoregressive model. The combination of complete vegetation control with fertilization results in the largest growth response; annual fertilization has the next largest growth response, with the exception that at very early stages the response is lower than that of vegetation control only; the control has the lowest growth response. The advantages of the multilevel nonlinear mixed effects model include its ability to handle unbalanced and incomplete repeated measures data, its flexibility to model multiple sources of heterogeneity and complex patterns of correlation, and its higher power to make treatment comparisons. We address in detail a general strategy of multilevel nonlinear mixed effects model building.
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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 | 0.003 | 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 | 0.000 |
| 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".