Additional file 1 of Quantifying the magnitude of the general contextual effect in a multilevel study of SARS-CoV-2 infection in Ontario, Canada: application of the median rate ratio in population health research
Bibliographic record
Abstract
Supplementary Material 1: Additional file 1: Figure S1, Ontario FS, Area (FSA), Map, Table S. Definitions of census-based area characteristics: constructs, statistical units, and operational definitions using the Ontario, Canada, census area profiles, 2016. Figure S2. Schematic diagram of the aggregated data structure used with Poisson multilevel models. Table S2. Ontario population study flow table. Figure S3. Graphical assessment of the linearity assumption of the FSA census-based continuous variables and the rate of SARS-CoV-2 infection using restricted cubic splines in the fully adjusted multilevel Poisson regression. Figure S4. Raw and Pearson Residuals vs Predicted Values Plots. Table S3. COVID-19 Wave 1 Analysis: Sequential multilevel Poisson count regression models for individuals with a SARS-CoV-2 infection in Ontario, Canada between March 1, 2020, and July 31, 2020. Table S4. COVID-19 Wave 2 Analysis: Sequential multilevel Poisson count regression models for individuals with a SARS-CoV-2 infection in Ontario, Canada, between August 1, 2020, and March 1, 2021. Table S5. COVID-19 Wave 3 Analysis: Sequential multilevel Poisson count regression models for individuals with a SARS-CoV-2 infection in Ontario, Canada, between March 2, 2021, and May 1, 2021. Table S6. Dissemination Area Sensitivity Analysis: Sequential multilevel Poisson count regression models for individuals with a SARS-CoV-2 infection in Ontario, Canada, between March 1, 2020, and May 1, 2021 (PDF 619 kb).
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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 | 0.003 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.737 | 0.060 |
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".