Estimation of population size from biased samples using non-parametric binary regression. Statistica Sinica
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Bibliographic record
Abstract
Abstract: We develop a new estimator of population size when data come from an independent double sampling experiment and at least one continuous covariate for each detection is measured. The new estimator has two features: (i) detection probabilities are estimated by non-parametric smoothing of redetections; (ii) pop-ulation size is estimated with a Horvitz-Thompson type estimator. Expressions for asymptotic bias and variance are developed. The estimators are shown to be effi-cient when sampling is unbiased. We provide an illustration on two-stage recapture data on aboriginals in Canada. Key words and phrases: Biased sampling, kernel regression, local linear estimator, Nadaraya-Watson estimator, wildlife abundance estimation. 1.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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