Decadal Trend in Humidex: Cooling and Heat Perception in Ibadan, Southwestern Nigeria
Bibliographic record
Abstract
Rising global temperatures have resulted in more frequent and intense weather phenomena like heatwaves, storms, and floods, with heatwaves posing notable risks to environment, agricultural and human health. Many studies use localized thresholds to assess heat indexes, taking into account the unique climate patterns of different regions. The Humidex, which measures thermal discomfort, provides a deeper understanding of how temperature and humidity combine to impact human comfort and environmental stress. Weather data from Ibadan, South-Western Nigeria, provided by the Nigerian Meteorological Agency (NIMET), were analyzed for key climate variables to calculate the Humidex. The Mann-Kendall test determined statistical relevance, while Sen's slope measured trend magnitude. Originally developed by Canadian meteorologists, the Humidex assesses how humidity impacts human comfort. The analysis of temperature, relative humidity, and Humidex trends from 2014 to 2023 shows a clear pattern of heat stress during the dry season (February-April) with some relief during the rainy season (June-August). The Mann-Kendall test and Sen's slope revealed a notable downward trend in temperatures, especially in January, March, June, and November, with decreases ranging from -0.08°C to -0.27°C annually. This cooling trend is mirrored by decreasing Humidex values, particularly in January, March and June, indicating reduced heat stress. High humidity levels during the rainy season are mitigated by the cooling effects of rainfall, leading to lower overall heat perception (Humidex). This relationship between temperature and humidity underscores their combined role in influencing heat stress and comfort levels. The annual variations emphasize the importance of monitoring and managing climate risks in regions prone to extreme heat and humidity. Overall, the decline in both temperature and Humidex suggests a shift toward cooler conditions, which could alleviate extreme heat but also prompt concerns about the broader impacts of these changes on regional climate and agriculture.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".