Mixture regression models for closed population capture–recapture data
Why this work is in the frame
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Bibliographic record
Abstract
In capture-recapture studies, the use of individual covariates has been recommended to get stable population estimates. However, some residual heterogeneity might still exist and ignoring such heterogeneity could lead to underestimating the population size (N). In this work, we explore two new models with capture probabilities depending on both covariates and unobserved random effects, to estimate the size of a population. Inference techniques including Horvitz-Thompson estimate and confidence intervals for the population size, are derived. The selection of a particular model is carried out using the Akaike information criterion (AIC). First, we extend the random effect model of Darroch et al. (1993, Journal of American Statistical Association 88, 1137-1148) to handle unit level covariates and discuss its limitations. The second approach is a generalization of the traditional zero-truncated binomial model that includes a random effect to account for an unobserved heterogeneity. This approach provides useful tools for inference about N, since key quantities such as moments, likelihood functions and estimates of N and their standard errors have closed form expressions. Several models for the unobserved heterogeneity are available and the marginal capture probability is expressed using the Logit and the complementary Log-Log link functions. The sensitivity of the inference to the specification of a model is also investigated through simulations. A numerical example is presented. We compare the performance of the proposed estimator with that obtained under model Mh of Huggins (1989 Biometrika 76, 130-140).
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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 | 0.001 | 0.002 |
| 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