Developing Forest Models from Longitudinal Data : A Case Study Assessing the Need to Account for Correlated and/or Heterogeneous Error Structures under a Nonlinear Mixed Model Framework(<Special Issue>Multipurpose Forest Management)
Bibliographic record
Abstract
In this study we demonstrated the procedures for model estimation and prediction based on the nonlinear mixed model (NLMM) technique. Since the unequally spaced and unbalanced longitudinal data used to fit forest models are often correlated and unequally varied, generalized error structures were examined and compared to the independent and identically distributed (iid) structure. In addition, since the vast majority of forest models are developed to be used as predictive tools on new data once the model coefficients have been estimated, predictions from the fitted model with and without accounting for the generalized error structures were evaluated on both model fitting and independent model validation data sets. Results showed that, under the NLMM framework, the iid structure is a superior choice for addressing correlated and heteroscedastic errors, provided that the model is appropriate for the data. This outcome has important practical implications, as a simpler error structure can achieve better predictions than more complex structures. The theoretical and practical consequences of ignoring or accounting for the error structure in NLMM estimation and prediction are discussed.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".