Spatiotemporal modeling of odds of disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The analysis of geographical and temporal variability of binomial data, using generalized additive mixed models, are considered. In this class of models, spatially correlated random effects and temporal components are adopted. The frequentist analysis of these complex models is computationally difficult. Recently developed method of data cloning has overcome the computational challenges of the analysis of mixed models from the frequentist approach. We use data cloning, which yields to maximum likelihood estimation, to propose frequentist analysis of spatiotemporal modeling of odds of disease. The advantages of the data cloning approach are that the prediction and prediction interval of the smoothing odds over space and time are easily obtained. We illustrate this approach using a real dataset of yearly asthma physician visits by children in the province of Manitoba, Canada, during 2000–2010. The performance of the proposed approach is also studied through a simulation study. Copyright © 2014 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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