A Multilevel Model for Continuous Time Population Estimation
Why this work is in the frame
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Bibliographic record
Abstract
Statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. Known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). As multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. However, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. Existing methods are based on time-to-event analyses with a second step of estimating population size. We propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. A Bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. The proposed method is applied to the motivating data to illustrate the methods' applicability. Then, a small simulation study explores the performance of the estimator under a variety of conditions. Finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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