Geostatistical Survival Models for Environmental Risk Assessment with Large Retrospective Cohorts
Bibliographic record
Abstract
Summary Motivated by the problem of cancer risk assessment near a nuclear power generating station, the paper describes a methodology for fitting a spatially correlated survival model to large retrospective cohort data sets. Retrospective cohorts, which can be assembled inexpensively from population-based health databases, can partially account for lags between exposures and outcome of chronic diseases such as cancer. These data sets overcome one of the principal limitations of cross-sectional spatial analyses, though performing statistical inference requires accommodating censored and truncated event times as well as spatial dependence. The use of spatial survival models for large retrospective cohorts is described, and Bayesian inference using Markov random-field approximations and integrated nested Laplace approximations is presented. The method is applied to data from individuals living near Pickering Nuclear Generating Station in Canada, showing that the effect of ambient radiation on cancer is not statistically significant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".