A new integrated likelihood for estimating population size in dependent dual‐record system
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Efficient estimation of the population size from dependent dual‐record system (DRS) remains a statistical challenge in the capture‐recapture type experiment. Owing to the non‐identifiability of the suitable time‐behavioural response variation model (denoted as M tb ) under DRS, few methods are developed in the Bayesian paradigm based on informative priors. Our contribution in this article is to develop a new integrated likelihood function from model M tb motivated by a novel approach developed by Severini ( 2007 ). A suitable weight function on the nuisance parameter is derived with the knowledge of the direction of behavioural dependency. A pseudo‐likelihood function is constructed so that the resulting estimator possess some desirable properties including negligible prior (or weight) sensitiveness. Extensive simulations show the superior performance of our proposed method to that of the existing Bayesian methods. Moreover, the proposed estimator is easy to implement from the computational perspective. Applications to two real data sets are presented. The Canadian Journal of Statistics 46: 577–592; 2018 © 2018 Société statistique du Canada
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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 | 0.002 |
| 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