Use of a dwelling-referenced geographic information system to characterize urban tuberculosis
Bibliographic record
Abstract
Using ArcView 3.2 software, all active TB cases reported in the former city of Montreal 1996--2000 were precisely geo-coded, that is mapped to the corresponding residential address. For comparison, using a case-cohort approach control dwellings were randomly selected from the municipal dwelling GIS, with a 10:1 ratio. We identified 595 case and 5950 control dwellings. Census tract data from the 1996 Canadian Census as well as dwelling characteristics from the Montreal housing database were attributed to both case and control dwellings. Multivariate logistic regression was used with dwelling status (case vs. control) as the dependant variable, to evaluate the independent influence of crowding and other socio-demographic factors. A high-precision housing GIS complemented census data in pinpointing and characterising the occurrence of TB in Montreal. It provided a more refined assessment of the impact of local crowding, after adjustment for other important factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".