Bibliographic record
Abstract
Today, the journal provides a platform to showcase and publish the work of the very diverse and specialized programs of the Public Health Agency of Canada, from the various branches at the Agency involved in infection prevention and control, including ACS from the National Advisory Committee on Immunization and reports from the Canadian Public Health Laboratory Network.CCDR publishes epidemiologic studies, eyewitness reports, implementation science research, outbreak reports, overviews, qualitative studies, rapid communication, surveillance reports, commentaries and several other types of articles.CCDR also publishes selected articles from the provincial, regional and local public health units, Canadian universities, and infectious disease departments from various hospitals across the country.Today, CCDR and the Health Promotion and Chronic Disease Prevention in Canada: Research, Policy and Practice (HPCDP Journal) are the two main, bilingual, peer-reviewed and open access scientific journals of the Public Health Agency of Canada.Together, we are proud to disseminate top quality Canadian data and findings to support evidence-informed discussions.We sincerely hope that our daily efforts contribute to informing, guiding and shaping public health actions, for the benefit of Canada and beyond.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".