A poststratified ratio estimator for model-assisted biomass estimation in sample-based airborne laser scanning surveys
Bibliographic record
Abstract
To estimate the aboveground biomass (AGB) for large areas, two-stage sampling designs using airborne laser scanning (ALS) as a strip sampling tool in combination with subsampling of field plots have been successfully applied in several studies. However, the studies have pointed to problems in the proposed estimator, partly related to the unequal length of flight lines in irregularly shaped areas. In this article, we present a model-assisted ratio estimator for such two-stage designs utilizing the area of the ALS strip as the auxiliary variable. The proposed estimator is further developed for estimation in subpopulations and for poststratified estimation. When deriving a variance estimator of the poststratified estimator, we considered the dependencies between estimates from different strata that arise since flight lines extend over several strata. An evaluation by simulated sampling in an artificial population based on data from a survey in Hedmark County, Norway, showed that the proposed estimators and their variance estimators performed well in the case of simple random sampling in both stages. In such cases, the ratio and poststratified estimators improved the precision of AGB estimates by 30% and 70%, respectively, in comparison with the earlier suggested estimator.
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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 | 0.007 | 0.023 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".