Evaluation of probability proportional to predictions estimators of total stem volume
Bibliographic record
Abstract
A plethora of probability proportional to predictions (PPP) estimators makes it hard for a user to decide which one to use. This study demonstrates the need for an extensive screening procedure by example of four PPP estimators of total stem volume and five estimators of sampling error. Bias, absolute bias, root mean square error, sample-based estimators of sampling error, and achieved significance levels of confidence intervals with a nominal significance level were compared across 832 distinct settings. Population size, sample size, the variance and skewness of the volume predictors, and the strength of the correlation and the slope between predicted and actual stem volume varied between settings. Estimators converged in performance as sample sizes increased but were otherwise sensitive to actual settings. Of the tested estimators, Brewer's "cosmetically calibrated" estimator was consistently the best in terms of mean absolute relative bias and generally favored in an overall assessment of five performance criteria. Grosenbaugh's adjusted estimator was a close second and was often ranked first in overall performance when n > 0.15N.
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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 | 0.061 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".