Bibliographic record
Abstract
Epidemiology Improvements in two very different methods of investigation led to better understanding of the dynamics of a tuberculosis (TB) outbreak in British Columbia, Canada. A 10-fold increase in TB cases was reported in a Canadian community between 2006 and 2008. Initial genotyping analysis by Gardy et al. suggested that the outbreak was clonal; however, whole-genome sequencing of M. tuberculosis isolates produced a different picture: The cases were the result of two outbreaks. Examination of historical isolates indicated that the two lineages were present before the recent outbreak, which suggested that it was a social or environmental effect, not a genetic mutation, which triggered the increase in cases. Social network analysis was then used to build a picture of risk behavior, interactions, and social meeting places. When this was combined with the whole-genome data, the investigators were able to identify sources of the outbreak and a likely contribution of increased crack cocaine use. As sequencing costs go down and network analyses become more sophisticated, it is likely that these strategies will be increasingly used in public health efforts. N. Engl. J. Med. 364 , 730 (2011).
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".