Combining forest growth models and remotely sensed data through a hierarchical model-based inferential framework
Bibliographic record
Abstract
Large-area growth estimates can be obtained by coupling growth model predictions with wall-to-wall remotely sensed auxiliary variables through a generalized hierarchical model-based (GHMB) inferential framework. So far, most GHMB variance estimators do not account for the residual errors of the submodels and their spatial correlations. This likely induces an underestimation of the true variance of the point estimator. In this study, we provide an example of large-area growth estimation obtained through the GHMB framework. To do this, we developed a new variance estimator that accounts for residual errors as well as potential spatial correlations among them. We tested this variance estimator through a simulation study and then used it to estimate the annual volume increment for a forest management unit in Quebec, Canada. Our results show that, contrary to our expectation, neglecting the residual errors of the different submodels leads to overestimating the true variance of the point estimator. We observed increases in the overestimation with small populations and spatially correlated residual errors. Our developed variance estimator corrected this overestimation and made it possible to derive reliable confidence intervals for annual volume increments at the population level.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".