population that is susceptible) can diff er according to population demographic structure.
Bibliographic record
Abstract
10 The 2009 infl uenza A (H1N1) virus had strikingly diff erent reproductive numbers, and thus very diff erent eff ects, in Indigenous Canadian populations and the general population of southern Canada. This diff erence might have resulted from diff erential crowding and a younger age distribution (those born before 1957 seem to have been protected against infection) in isolated First Nations reservations. 11 If the mean value of R0 is fi xed, heterogeneity caused by diff erences between individuals in one setting (eg, superspreaders 5 ) or by diff erences between settings (hospital vs community) both increase variation in cluster size and reduce the probability that any particular individual infection will cause an epidemic. 4 Pandemic risk estimates based on early, scarce information should be interpreted with caution, because the identifi cation of highly infectious individuals and severe cases is more likely, and because of the greater availability of surveillance resources in high-income populations where transmission characteristics of patho gens might be atypical. 12
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".