SUPPLEMENTAL METHODS Urban Contact Network Generation
Bibliographic record
Abstract
For this study, we generate plausible contact networks for an urban setting using demographic information for the Greater Vancouver Regional District, which is the third largest metropolitan area in Canada. We use publicly available data from sources such as Statistics Canada to estimate the distribution of ages, household sizes, school and classroom sizes, hospital occupancy, workplaces, and public spaces [S1-S4]. Qualitatively similar age and household size distributions are found for other cities in Canada ranging in population sizes from 120,000 to 4.6 million [S5]. We begin assembling the urban network by choosing 100,000 households at random from the Vancouver household size distribution [S1], which yields approximately 257,000 people according to a mean household size of approximately 2.6. Based on ages assigned from the measured Vancouver age distribution [S2], each member of the population is assigned to activities: to schools according to school and class size distributions [S3]; to occupations according to (un)employment data; to hospitals as patients and caregivers according to hospital employment and bed data [S4]; to nursing homes according to nursing home occupancy data; and to other public places. To model heterogeneities in contact patterns, we create random connections (edges)
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.139 | 0.021 |
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".