Bibliographic record
Abstract
CircHOB is an international, open access, collaborative health information system, involvedin systematic, standardized, and consistent data collection and analysis. It is population-based, and covers all northern regions in all circumpolar countries. CircHOB’s purpose is to monitortrends and patterns in health status, health determinants, and health care, and provide an ongoingand sustainable knowledge base and analytical support for researchers, statisticians, healthcare providers, public health practitioners and policy makers. CircHOB also serves as a resourcefor training and research in population health and health systems and enhances partnerships andcollaborations among health and statistical agencies in circumpolar countries and regions.CircHOB extends and updates the data tables, charts and maps originally published in CircumpolarHealth Indicators as a Circumpolar Health Supplement, the sister publication of the Journal.Currently, its website provides access to the 2000–2004 datasets and associated thematic mappingtools based on UNESCO’s Flash-based StatPlanet software. The datasets are currently beingupdated to 2009 and will also be presented for download and interactive visualization. Referencematerials and statistical reports related to the data are also being catalogued for a fully searchableonline document database.CircHOB is hosted at the Institute for Circumpolar Health Research data center [www.ichr.ca].ICHR research affiliates and staff are responsible for database development and maintenance, dataretrieval, analysis, and presentation.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.105 | 0.047 |
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".