Bibliographic record
Abstract
Maintaining a clean water supply is of utmost importance for human civilization. Human activities are putting an increasing strain on Earth's freshwater reserves and on the quality of available water on Earth. To ensure cleanliness and potability of water, sensors are required to monitor various water quality parameters in surface, ground, drinking, process, and waste water. One set of parameters with high importance is the presence of cations. Some cations can play a beneficial role in human biology, and others have detrimental effects. In this review, various lab-based and field-based methods of cation detection are discussed, and the uses of these methods for the monitoring of water are investigated for their selectivity and sensitivity. The cations chosen were barium, cadmium, chromium, copper, hardness (calcium, magnesium), lead, mercury, nickel, silver, uranium, and zinc. The methods investigated range from optical (absorbance/fluorescence) to electrical (potentiometry, voltammetry, chemiresistivity), mechanical (quartz crystal microbalance), and spectrometric (mass spectrometry). Emphasis is placed on recent developments in mobile sensing technologies, including for integration into microfluidics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".