Inferring population size: extending the multiplier method to incorporate multiple traits with a likelihood‐based approach
Why this work is in the frame
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Bibliographic record
Abstract
Estimating population size is an important task for resource planning and policy making. One method is the “multiplier method” that uses information about a binary trait to infer the size of a population. This paper presents a likelihood‐based estimator that generalizes the multiplier method to accommodate multiple traits as well as any number of categories in a trait. To provide guidelines for study design, we quantify the advantage of using multiple traits (multiple multipliers) by studying the estimator's asymptotic standard deviation (ASD). Inclusion of multiple traits reduces the ASD most effectively when the traits are uncorrelated and of low prevalence (roughly less than 10%), but the amount of reduction in ASD diminishes when the number of traits becomes large. A Bayesian implementation of our method is applied to both simulated data and real data pertaining to an injection‐drug user population. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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