Unequal probability sampling in fixed area plots of stem volume with and without prior inclusion probabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The impact of guessing auxiliary population attributes, as opposed to relying on actual values from a prior survey, was quantified for three unequal probability sampling methods of tree stem volume (biomass). Reasonable prior guesses (no-list sampling) yielded, in five populations and 35 combinations of population size and sample size, results at par with sampling with known auxiliary predictors (list sampling). Realized sample sizes were slightly inflated in no-list sampling with probability proportional to predictions ( PPP ). Mean absolute differences from true totals and root mean square errors in no-list-sampling schemes were only slightly above those achieved with list sampling. Stratified sampling generally outperformed PPP and systematic sampling, yet the latter is recommended due to consistency between observed and expected mean square errors and overall robustness against a systematic bias in no-list settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it