Impact of Climate Variability on Human Health in Ilorin, Nigeria
Bibliographic record
Abstract
Climate change is a global issue and its impact is felt everywhere by both human and ecosystem. Climate variability and change threaten the well being of humans. This study examines the impact of climate variability on human health with the use of regression, correlation and ANOVA. The result shows that there is a very strong positive correlation between minimum temperature and typhoid (0.844), maximum temperature and malaria (0,794), typhoid (0.793), between sunshine and typhoid (0.667), malaria (0.630). The other variables are weakly correlated with the diseases. The regression analysis reveals that 49%, 88% and 79% of the variance in asthma, typhoid and malaria can be respectively explained by the climatic parameters under study to a certain extent. This implies that there may be some other factors that are responsible for the selected diseases in the area. Such factors may include biological (genotype, micro-organisms, and allergies), unhygienic environment and economic (poor living conditions). Recommendations made include weather report should be broadcasted to people through the media in order for them to understand variation in the climate and how to adapt and mitigate the effect of the changes. Furthermore, people should be enlightened on the effects of anthropogenic activities in the atmosphere and how to reduce these effects for sustainable development.
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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 | 0.001 |
| 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".