Research on evaluating water pollution determinants using multiple logistic regression
Bibliographic record
Abstract
Water pollution is a pivotal challenge, underpinning urgent conversations around environmental sustainability, public health, and ecosystem viability. This research aims to assess the degree of water pollution, dissect and understand the myriad factors contributing to it, and pave the way for formulating effective mitigation strategies and policies to preserve the integrity of water bodies worldwide. It highlights that rapid industrialization, population growth, and agriculture cause pollution. Industrial activities release pollutants like heavy metals, while agriculture contributes through runoff. Urbanization also exacerbates the problem. The study uses a dataset from Kaggle and selects variables like aluminium, ammonia, etc. A multiple logistic regression model analyses factors affecting water potability. Results show that aluminium, chloramine, and ammonia positively correlate with potability, while uranium and barium have negative ones. Interaction terms added to the model improve its fit. The study emphasizes understanding individual contaminants and their interactions for effective water management strategies. Accounting for these interactions enables a more comprehensive understanding of the factors affecting water safety. These insights are crucial for developing targeted and effective water management strategies that ensure safe drinking water and support public health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
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".