Bibliographic record
Abstract
Abstract Terrestrial life may be carbon-based, but most of its mass is made up of water. Access to clean water is essential to all aspects of maintaining life. Mainly due to human activity, the strain on the water resources of our planet has increased substantially, requiring action in water management and purification. Water quality sensors are needed in order to quantify the problem and verify the success of remedial actions. This review summarizes the most common chemical water quality parameters, and current developments in sensor technology available to monitor them. Particular emphasis is on technologies that lend themselves to reagent-free, low-maintenance, autonomous and continuous monitoring. Chemiresistors and other electrical sensors are discussed in particular detail, while mechanical, optical and electrochemical sensors also find mentioning. The focus here is on the physics of chemical signal transduction in sensor elements that are in direct contact with the analyte. All other sensing methods, and all other elements of sampling, sample pre-treatment as well as the collection, transmission and analysis of the data are not discussed here. Instead, the goal is to highlight the progress and remaining challenges in the development of sensor materials and designs for an audience of physicists and materials scientists.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".