Plant-Capture Methods for Estimating Homeless Population Size From Uncertain Plant Captures
Bibliographic record
Abstract
Plant-capture is a specialized variant of traditional capture-recapture methods used to estimate the size of a population. In epidemiologic literature, a notable application of this method is the estimation of the size of homeless populations through point-in-time street surveys. With this approach, decoys referred to as "plants" are introduced into the population to estimate the capture probability. Previous plant-capture studies have not systematically accounted for uncertainty in the capture status of individual plants. To address this, we propose three increasingly complex hierarchical modeling approaches to formally incorporate uncertainty into the plant-capture model arising from the capture status of plants and heterogeneity between survey sites. We then apply our methods to estimate the size of the homeless population in large US cities in the context of the "S-Night" study conducted by the US Census Bureau. Details on the frequentist and Bayesian implementations of our models, along with empirical evaluations of their statistical performance, are provided in the supplementary materials.
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How this classification was reachedexpand
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".