Model-assisted forest inventory with parametric, semiparametric, and nonparametric models
Bibliographic record
Abstract
Survey sampling with model-assisted estimation has been gaining popularity in forest inventory recently, as the availability of cheap, good-quality remotely sensed data that can be used as auxiliary information has improved. Most of the studies have been carried out using parametric (linear or nonlinear) models. However, nonparametric and semiparametric models such as k nearest neighbor, kernel, and generalized additive are widely used in forest inventory. The results are usually calculated using the difference estimator (i.e., assuming an external model), even though the models used are based on the sample (i.e., an internal model). In that case, variances will likely be underestimated. In this study, we analyze how well the difference estimator works for different types of models, both internal and external. The study is based on simulated populations produced using a C-vine copula model with empirical marginals. The external model is based on real data, and the internal models are estimated from samples from the simulated population. The results show that the analytical variance estimates for a difference estimator based on an overfitted kernel model can seriously underestimate the true variance.
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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 | 0.018 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".