Seasonal And Regional Effects of Air Quality Index on Hematological Indices of Dogs Under Local Environmental Conditions In Pakistan
Bibliographic record
Abstract
Air pollution is found to have significant association with living health all over global world. Environment Protection Department of Punjab, Pakistan provides the monthly air quality index (AQI) data on air pollution with concentrated particulates like PM2.5, PM10, NO2, SO2 and O3. Air particulates concentrations may vary of season, regional geography and climate. We performed blood sampling of 45 dogs from different breeds (Labrador retrievers, German shepherds and Pit bulls) from three different areas categorized on basis of AQI as less polluted (Gulberg), polluted (Town Hall) and highly polluted (Shadman) in winter and spring seasons. Data were analysed using paired sample t-tests for seasonal study and independent sample t-tests for area and breed study by SPSS (P < 0.05 ascertained as significant). Seasonal study resulted that less polluted area had no significant effects on hematological indices in any three breeds of dogs in both seasons. Polluted area was presented with increased significant effects on values of monocytes in Labrador retrievers, Hct and MCHC in German shepherds while decreased significant effects on MCH value in Pit bulls in winter season as compared to spring season. Highly polluted area had significant effects with decreasing WBC counts only in German shepherds in winter season than spring season. This study concludes that polluted and highly polluted areas in winter season with worst AQI affects blood indices more than spring season in dogs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".