An analysis and comparison of predictors of random parameters demonstrated on planted loblolly pine diameter growth prediction
Bibliographic record
Abstract
Non-linear mixed effects (NLME) models are now common in the forestry literature. The fixed effects parameters are estimated in the model fitting stage. For new individuals, the random effects parameters are predicted when the model is used to predict the response variable. Predicting both the random effects parameters and the response variable requires the underlying model to be linearized using a Taylor series expansion. This is done by expanding around the expected or predicted value of the random effects parameters. Whichever way is chosen, it should be consistent. However, mismatches between the predictors for the random effects and predictors for the future response are sometimes made in forestry growth and yield applications. We consider the implications of using mismatched predictors on a single-level NLME model. We analyzed and empirically compared a total of four combinations of two types of predictors for both the random effects and the response variable. Diameter at breast height was predicted for 140 loblolly pine trees using these four combinations of predictors. The predictors were evaluated with the mean squared error. Model accuracy was best when the predictors for the random effects and the response variables were based on the same expansion method.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".