Varuna: A Python-Based System for Water Quality Index Calculation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Varuna, developed by Joyanta Debnath and Ananta Debnath, is a Python-based system for calculating water quality indices, including the Water Quality Index (WQI), Canadian Water Quality Index (CWQI), and National Sanitation Foundation (NSF) Water Quality Index. The system assesses water body health by integrating physical, chemical, and biological parameters, aiding researchers and professionals in identifying pollution issues and prioritizing sustainable management. Hosted on GitHub (https://github.com/JoyantaDeb/Varuna-Water-Quality-Index), Varuna offers a user- friendly interface, interactive Matplotlib visualizations, and customizable inputs for local conditions. As an open-source platform, it fosters global collaboration. This paper details Varuna’s development, calibration, coding implementation, and applications in sustainable water management, contributing to environmental protection.
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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 | 0.001 | 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) | 0.000 | 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 it