Bibliographic record
Abstract
Sampling with probability proportional to prediction (3P sampling) is useful where the variable of interest to a forest inventory is costly to measure and where there exists a cheaper to measure auxiliary variable, which correlates positively with the variable of interest. Two forms of 3P sampling, termed “classical” and “point-” 3P sampling, have received some use in forest inventory. However, both have limitations that have restricted their use mainly to estimation of tree stem wood volume for timber sales over small forest areas in North America. A more general form of 3P sampling, termed here “ordinary” 3P sampling, has been all but ignored to date. It has potential for use in inventory of a broad range of forest attributes, both floral and faunal and both commercial and environmental, across large or small forest areas. Using a common mathematical approach, the present work derives the estimators of the population mean for these three forms of 3P sampling. Their properties are compared with simple random sampling through Monte Carlo simulations based on two example forest populations. The work lays a basis from which 3P sampling might develop further and enjoy wider application in forest inventory than has been the case previously.
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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 | 0.040 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".