Considerations for new manganese analytical techniques for drinking water quality management
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
Abstract Manganese (Mn) is a contaminant of emerging concern in drinking water, as recent epidemiologic evidence suggests an association between Mn exposure in drinking water and negative neurodevelopmental effects. The nature of Mn events in distribution systems can be sporadic and difficult to predict, with conventional laboratory methods being limited in their ability to provide the flexible on‐line Mn monitoring. Emerging methods such as colorimetric and electrochemical methods offer advantages for monitoring as they have potential to be less expensive, rapid, and readily deployed in the field. These emerging methods, however, face hurdles to adaptation and acceptance including demonstration of sufficient accuracy, precision, sensitivity and yet‐to‐be resolved issues with interfering agents. These hurdles are not insurmountable, and investment is warranted in these novel methods to address pressing needs by the water industry to protect human health. This review paper highlights the opportunities and advantages of advancing field‐testing techniques for Mn management.
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.
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 | 0.001 | 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