Incorporating correlated error structures into mixed forest growth models: prediction and inference implications
Bibliographic record
Abstract
Three nonlinear mixed models with and without incorporating a function to model the serial correlation were compared with regard to their predictive abilities. Results showed that accounting for the serial correlation using the spatial power (SP(POW)) or Toeplitz (TOEP(X)) functions resulted in a large reduction in serial correlation and improved the fit of the models. The improved model fits, however, did not unanimously translate into improved model predictions when evaluated under different scenarios. In many cases, the models with the simple independent and identically distributed structure outperformed the models with the SP(POW) or TOEP(X) structure in terms of the models’ predictive ability. We also examined the effect of adjusting predictions based on the prediction theorem within the nonlinear mixed modeling framework. It was shown that, in general, the adjusted predictions had lower errors than those without adjustment, but the differences were small in many cases. The adjustment with three prior measurements was better in predictions than the adjustment with only one or two prior measurements for the models with the TOEP(X) structure, but not for SP(POW). A theoretical derivation was developed to prove the insensitivity of the models with the SP(POW) structure to the number of prior measurements. The implications of accounting for serial correlation on model inference and model predictions were 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 | 0.034 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".