IDENTIFYING COVIDOGENIC ENVIRONMENTS IN URBAN SECTORS OF KHROUB CITY (ALGERIA): A GIS-BASED APPROACH TO ASSESSING PANDEMIC RISK AND VULNERABILITY
Bibliographic record
Abstract
This study aims to assess the pandemic risk in the Algerian city of Khroub and develop a monitoring and health management tool to combat Covid-19 and other respiratory infections. To address the lack of statistical data at the micro-urban level, the authors conducted a household survey in Khroub between July and September 2022. The primary objective of this survey was to collect comprehensive data on vulnerability indicators at the scale of Khroub's urban sectors. The study utilized 13 indicators of vulnerability to Covid-19, selected from previous studies and research published by public health organizations and agencies. GIS technology was used to locate covidogenic environments (milieu) in Khroub, resulting in the creation of a GIS database called "Covidogenic Milieu." This study provides valuable insights for identifying vulnerable urban sectors and implementing adaptive measures to mitigate the effects of Covid-19. In the case of Khroub, the research also made relevant suggestions on how to address the identified vulnerability for the benefit of local authorities who commissioned this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".