Advancing performance evaluation standards for household water treatment technologies
Bibliographic record
Abstract
Abstract Diarrheal illnesses and fatalities continue to be major issues in many regions throughout the world. Household water treatment (HWT) technologies (including both point-of-use (POU) and point-of-entry (POE) treatment solutions) have been shown as able to deliver safe water in many low-income communities. However, as shown herein, there are important inconsistencies in protocols employed for validating performance of HWTs. The WHO does not stipulate influent concentration as a parameter that could influence removal efficacy, nor does it indicate an influent concentration range that should be used during technology evaluations. A correlation between influent concentration and removal is evidenced herein (R2 = 0.88) with higher influent concentrations resulting in higher log-removal values (LRVs). The absence of a recommended standard influent concentration of bacteria (as well as for viruses and protozoa) could have negative consequences in intervention efforts. Recommendations are provided that regulatory bodies should specify an influent concentration range for testing and verification of HWT technologies.
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 | 0.237 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".