Bibliographic record
Abstract
This thesis contains three separate chapters motivated by research questions related to public health. The first contribution is in the second chapter, which introduces a novel parameter transformation of anisotropic covariance functions. This transfor- mation relates the anisotropic ratio with the polar radius, and the anisotropic angle with the polar angle. It resolves an issue with the standard parameterization where the anisotropic angle has no meaning in the special case of isotropy, and provides a firmer mathematical framework in which to view isotropic covariance functions as a special case of the anisotropic covariance functions. It also proposes a transformation of the standard deviation and range parameters that renders them approximately orthogonal. The third chapter models the time-varying effect of air pollution. There is increasing interest in modeling air pollution effects as time-varying, and the contribution of this chapter is in proposing two posterior statistics that summarizes its trend over time – stability and momentum. Stability is the standard deviation of== tthis effect over time, and momentum is the proportion of its pairs of differences that are positive. The fourth chapter proposes a model that decomposes daily COVID-19 mortality into the sum of (scaled) skew normal curves. COVID-19 mortality has been heavily studied in recent years, and the contribution of this model is in providing the evolution of these curves and their parameters over a multi-year period. The model has interpretable parameters, including a shape parameter, which quantifies the degree of asymmetry of each skew normal curve. In addition to the methodological developments listed above, the models in chapters three and four provide results that (pending peer review) may be of interest to practitioners. The trend detection model is fit to daily air pollution and mortality data in census divisions across Canada, and finds one region with an increasing trend and one with a decreasing trend. The skew normal model provides a decomposition of daily COVID-19 mortality into skew normal curves in six regions, and the relation of these curves to COVID-19 waves may be of independent interest. Moreover, both of these models can be applied to any health outcome of interest.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".