Bibliographic record
Abstract
The spreadsheet "masks" uses a simple next generation approach to explore the potential impact of mask use on COVID-19 R0 in communities.<br>Masks are effective in 2 ways: they prevent infection with COVID-19 in wearers; and prevent transmission by individuals with subclinical infection. The model permits assortative mixing (sheet = "Assortative"), based on the epsilon approach of Garnett and Anderson (https://academic.oup.com/jid/article/174/Supplement_2/S150/884972). The identity matrix in the extreme disassortative case is replaced by a matrix with zeroes in the diagonals.<br>The parameter "eta" defines assortativity in the assortative case (0 = random, 1 = extreme assortative), and disassortativity (0 = random, 1 = extreme disassortative) in the disassortative case.<br>For those unfamiliar with the next generation approach I have also included some classroom materials and a toy spreadsheet from the Dalla Lana School of Public Health, University of Toronto.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.033 |
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; both teacher heads agree on what is shown here.
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".