An evaluation of defluoridation technologies in the context of decision-making strategies
Bibliographic record
Abstract
Fluoride can be considered beneficial or harmful to human health dependant on the amount that is ingested. Many methods exist for the removal of excessive fluoride from drinking water; however, not all defluoridation methods are appropriate for the specific local conditions that may be present within a community. The objective of this research project is to evaluate various available defluoridation technologies against characteristics of a successful and sustainable technology, while considering decision-making strategies that may be employed by those selecting an appropriate defluoridation method to suit the characteristics of the specific community that is affected by fluoride-impacted drinking water. An evaluation of the selected defluoridation technologies supports the notion that there is not an all-encompassing defluoridation technology that would be applicable to all types of situations in which fluoride impacted groundwater is a concern. Water treatment methods for developing countries require careful consideration and selection of a sustainable solution so as to provide long-term benefits and applicability. Formal decision-making strategies would be useful tools at the government level to provide a starting point to determine which available defluoridation methods would be viable at the end user level. Researchers, government officials and, most importantly, local inhabitants of suffering areas must work together to achieve the common goal of clean and safe potable water.
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 | 0.032 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".