The Impact of Climate Change on the Microbial Content of Groundwater Resources: A Case Study on Escherichia coli Bacteria
Bibliographic record
Abstract
This study aimed to demonstrate the impact of climate change, represented by changes in temperature and rainfall rates, on the microbial content of Escherichia coli bacteria in the water treated by the Faisal Well Water Treatment Plant in Jerash city. The study introduced statistical evidence to provide a comprehensive understanding of the impact of climate change on the microbial content of the water treated by the investigated plant. The results of the study showed that the increase in the ambient temperature and the variation in the rainfall rate in the study area, during the years 2010-2024, led to a significant increase in the microbial content of Escherichia coli bacteria in the water, which negatively affects the quality of this water, as the results showed that the numbers of the bacteria bacilli under study had doubled to 13 times in 2024 compared to what they were when the plant started operating in 2010, which threatens the possibility of relying on this source as a reliable source of drinking water and other uses. The study came out with a set of recommendations, the most important of which is the need to work on adopting methods to mitigate the impacts of climate change on the increase in microbial content in water resources, and to enhance measures to adapt to these impacts at the national level, as well as the need to focus on conducting studies and researches that address the impact of the phenomenon of climate change on water resources, and designing practical strategies to mitigate and adapt to this phenomenon to reduce its negative consequences on public health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".