Hybrid estimation based on mixed-effects models in forest inventories
Bibliographic record
Abstract
In forest inventories, there are many variables of interest that are difficult to measure. Practitioners have to rely on auxiliary variables and models to obtain predictions of these variables. In such contexts, design-based or model-dependent inferences are often ineffective and hybrid estimators are required. Because most models now contain mixed effects, we investigated how the random effects and residual errors affected the inferences in a context of hybrid estimation. We first developed hybrid estimators for the different mixed models. We then tested these estimators through a simulation study. Finally, the estimators were applied to a real-world case study: stone pine (Pinus pinea L.) cone production in central Spain. It turned out that the contributions of the random effects and the residual errors to the variance were constant regardless of the sample size. In our case study, these contributions were rather small when compared with those of the sampling and parameter estimates. The greatest impact came from the underestimation of the variance of the parameter estimates when random effects were not taken into account in the model. As the variance estimators make it possible to distinguish different variance components, they can be useful for identifying the greatest sources of uncertainty.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".