Updating plots to improve the precision of small-area estimates: the example of the Lorraine region, France
Bibliographic record
Abstract
The sampling intensity of a national forest inventory is usually low. Forest dynamics models can be used to update plots from past inventory campaigns to enhance the precision of the estimate on smaller areas. By doing this, however, the inference relies not only on the sampling design, but also on the model. In this study, the contribution of model predictions to the variance of enhanced small-area estimates was assessed through a case study. The French national forest inventory provided different annual campaigns for a particular region and department of France. Three past campaigns were updated using a forest dynamics model, and estimates of the standing volumes were obtained through two methods: a modified multiple imputation and the Bayesian method. The update greatly increased the precision of the estimate, and the gain was similar between the two methods. The sampling-related variance represented the largest share of the total variance in all cases. This study suggests that plot updating provides more precise estimates as long as (i) the forest dynamics model exhibits no systematic lack of fit and was fitted to a large data set and (ii) the sampling-related variance clearly outweighs the model-related 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".