Sampling estimators of total mill receipts for use in timber product output studies
Bibliographic record
Abstract
Data from the 2001 timber product output study for Georgia was explored to determine new methods for stratifying mills and finding suitable sampling estimators. Estimators for roundwood receipts totals comprised several types: simple random sample, ratio, stratified sample, and combined ratio. Two stratification methods were examined: the Dalenius–Hodges (DH) square root of the frequency method and a cluster analysis method. Three candidate sizes for the number of groups were selected from the cluster analysis and subsequently used in the DH stratification as well. Relative efficiency improved when the number of groups increased and when using a ratio estimator, particularly a combined ratio. The two stratification methods performed similarly. Neither the DH method nor the cluster analysis method performed better than the other. Six bound sizes (1%, 5%, 10%, 15%, 20%, and 25%) were considered for deriving samples sizes for the total volume of roundwood receipts. The minimum achievable bound size was found to be 10% of the total receipts volume for the DH method using a two-group stratification. This was true for both the stratified and combined ratio estimators. In addition, for the stratified and combined ratio estimators, only the DH method stratifications were able to reach a 15% bound on the total (six of the 12 stratified estimators). These results demonstrate that the utilized classification methods are compatible with stratified totals estimators and can provide users with the opportunity to develop viable sampling procedures as opposed to complete mill censuses.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".