Understanding the State of Sanitation in India Through Qualitative Methods and a Septic Tank Sensing Device
Bibliographic record
Abstract
Abstract Worldwide, 3.6 billion people lack access to safely managed sanitation. India bears the brunt of this statistic, with over 395 million citizens lacking access today. Currently, Phase 2 of the Clean India Mission program focuses on connecting septic tanks to treatment facilities. However, according to previous studies, households only empty their septic tanks once they become completely full. A septic tank can only function properly until it is at approximately 70% of its capacity, and it is recommended that tanks should be emptied every 3 to 5 years. The authors hypothesize that a device to detect when a septic tank should be emptied could encourage households to empty their tanks on time. To gain a better understanding of how such a device would fit into the broader sanitation chain and the requirements of this device, a field study encompassing interviews, questionnaires, and septic tank data collection was conducted in three municipalities along the Warna River in Maharashtra, India. The results of the study reveal that the proposed device should only be introduced once a municipality has access to a functional fecal sludge treatment facility. Once a treatment plant has been built, depending on the municipality, different stakeholders need to be engaged in the device’s installation. Finally, the proposed device should detect a septic tank’s hydraulic retention time rather than the number of years since it was last emptied. This device will enable the Indian government to successfully implement Phase 2 of the Clean India Mission by alerting households to empty their tanks.
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 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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".