Review of global sanitation development
Bibliographic record
Abstract
The implementation of the United Nations (UN) Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs) has resulted in an increased focus on developing innovative, sustainable sanitation techniques to address the demand for adequate and equitable sanitation in low-income areas. We examined the background, current situation, challenges, and perspectives of global sanitation. We used bibliometric analysis and word cluster analysis to evaluate sanitation research from 1992 to 2016 based on the Science Citation Index EXPANDED (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) databases. Our results show that sanitation is a comprehensive field connected with multiple categories, and the increasing number of publications reflects a strong interest in this research area. Most of the research took place in developed countries, especially the USA, although sanitation problems are more serious in developing countries. Innovations in sanitation techniques may keep susceptible populations from contracting diseases caused by various kinds of contaminants and microorganisms. Hence, the hygienization of human excreta, resource recovery, and removal of micro-pollutants from excreta can serve as effective sustainable solutions. Commercialized technologies, like composting, anaerobic digestion, and storage, are reliable but still face challenges in addressing the links between the political, social, institutional, cultural, and educational aspects of sanitation. Innovative technologies, such as Microbial Fuel Cells (MFCs), Microbial Electrolysis Cells (MECs), and struvite precipitation, are at the TRL (Technology readiness levels) 8 level, meaning that they qualify as "actual systems completed and qualified through test and demonstration." Solutions that take into consideration economic feasibility and all the different aspects of sanitation are required. There is an urgent demand for holistic solutions considering government support, social acceptability, as well as technological reliability that can be effectively adapted to local conditions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".