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
A Horvitz–Thompson‐type estimator of species richness for plot (cluster) sampling is constructed by considering species sampling as sampling with an unequal probability. Inclusion probabilities are estimated from sample‐based estimates of relative species incidence. Bias is addressed by adding, to each observed species, the expected number of unseen species with the same relative incidence. A Hansen–Hurwitz estimator of variance is adopted and augmented by the anticipated variance from sample‐based inclusion probabilities and the number of observed species. In Monte Carlo simulation of simple random plot (cluster) sampling from 11 large finite populations of forest trees and three sample sizes, the proposed estimator achieved the best overall ranking in terms of relative root mean square error efficiency when compared to 12 alternative estimators. The proposed estimator ranked third in terms of bias. The augmented Hansen–Hurwitz estimator of variance was liberal (median −13%). No richness estimator was uniformly best across populations and sample sizes. Across all settings, the performance of the best four estimators was similar, both in terms of bias and relative root mean square error efficiency. Copyright © 2011 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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 it