CliFin
Bibliographic record
Abstract
Over the past decade, the development of web-based Geographic Information Systems (GIS) for health has grown quite rapidly due to an increased need of data integration and spatial visualization. One GIS growth area in health is the construction of map-based applications that provide information on health care resources. Such applications are typically used as standard tools by public health departments, public health policy and research organizations, hospitals and health insurance organizations to provide public access to health care resources. This paper presents the design and development process of Clinic Finder (CliFin) - an open-access web-based GIS application relying on the Google Maps technology and providing access to a database with point of care facilities across the Province of New Brunswick, Canada. The uniqueness of CliFin consists in the implementation of a time-frame dependent search and results trimming approach, which allows users to identify clinics and hospitals open at any given time. The users are also encouraged to contribute with schedule updates and new point of care information to further develop CliFin’s database and its accuracy. The combination of GIS visualization capabilities, database management, user involvement in database update and the time-frame dependence of search results, confers CliFin increased practicality, especially in situations of crisis such as natural disasters.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.254 | 0.101 |
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".