Overview of electronic tongue sensing in environmental aqueous matrices: potential for monitoring emerging organic contaminants
Bibliographic record
Abstract
Emerging organic contaminants (EOC) are synthetic or naturally occurring chemicals that have the potential to enter the environment and cause known or suspected adverse ecological and human health effects. Despite not being commonly monitored, EOC are often detected in effluents and water bodies because of their inefficient removal in conventional wastewater treatment plants. There is a growing concern about the presence and impact of EOC as well as the need for reliable and effective water monitoring using sensors capable of detecting the target molecules in complex media. Due to their specificities, such as fast response times, low cost, portability and user-friendly operation, electronic tongue (e-tongue) systems present some advantages over the traditional analytical techniques (e.g., chromatographic systems) used for environmental monitoring. We reviewed e-tongue sensors, focusing on their ability for real-time environmental monitoring. A bibliometric evaluation was carried out, along with a study of the status of the existing e-tongue systems, how they worked, and their applications in different fields. The potential of e-tongue sensors to detect organic contaminants in aqueous environmental matrices is discussed, with a particular focus on EOC.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".