Assessing Avian Influenza Vulnerability Using Geographically Weighted Regression, Batna Algeria
Bibliographic record
Abstract
Avian Influenza Virus (AIV) is a serious public health problem. The main causes have not yet been precisely detected. This work aims to map the AIV vulnerability based on the geographical relationships between environmental key factors and the vulnerability axis. Modeling the different predictors of AIV vulnerability is very important because the rate of contamination increases with the availability of suitable conditions. We deduced a statistical model from the geographically weighted regression results to predict the vulnerability of AIV through the district of Batna as a function of Six environmental variables (slaughterhouses, normalized difference of vegetation index, slope, temperatures, markets, and road density) and indicates that the vulnerability is highest in the Central and Eastern municipalities with more than 61% of the total area at medium or high AIV vulnerability. These results represent a modest contribution that may open a new path for environment-epidemiology relationships and associated geo-analytical databases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.024 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".