Estimating a Nonlinear Mixed Volume-age Model with and without Taking into Account Serially-correlated Errors: Differences and Implications
Bibliographic record
Abstract
In this study we estimated a lodgepole pine (Pinus contorta var. latifolia Engelm.) volume-age model with and without taking into account serially-correlated errors arisen from permanent sample plots. The estimations were based on the first-order (FO) and first-order conditional expectation (FOCE) methods of the nonlinear mixed model technique. Among the correlated error structures considered, the spatial power structure was found to be the most appropriate. Model predictions were obtained and evaluated on model fitting data, as well as on independent validation data collected from a different ecoregion. Results showed that the model estimated with the independent and identically distributed (iid) error structure performed much better than the model estimated with the correlated error structure. This is true on both model fitting and validation data sets, and for both FO and FOCE methods. It implies that, if the main purpose of a study is to develop models for predictions, there is no real benefit to consider more elaborate and complex error structures to account for the correlated errors. The iid error structure is a sound choice for dealing with correlated errors under the nonlinear mixed model framework.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".