Detection methods of carcinogens in estuaries: a review
Bibliographic record
Abstract
About 70% of all cancer deaths occur in low-and middle-income countries. Deaths from cancer worldwide are projected to continue to rise over 13.1 million by 2030. Research indicates that estuaries such as rivers and lakes contain various carcinogenic compounds. The carcinogens are transported from the release points to the intake points. To avoid this phenomenon from happening, fast and accurate detection of carcinogen concentrations in water is crucial. This paper presents a review of various detection methods of carcinogenic compounds in watercourses via biological, physical and chemical approaches. Descriptions of the biological procedures such as chromosomal aberration, Micronucleus assay, Ames test, Salmonella assay, SOS chromotest, Comet assay and organic extraction of XAD with blue rayon are discussed. Physical approaches have the integration of automated detections with science and engineering approaches, particularly sensors. Automated optical sensors, submersible ultraviolet fluorometer sensors, photoelectrochemical sensors with the detection of DNA damage and trihalomethanes sensors for the detection of carcinogenic compounds are also reviewed. Chemical approaches consist of the analysis of extraction, purification and identification of organic contaminants using high-technology equipments such as the atomic absorption spectrophotometer, high-performance liquid chromatography and gas chromatography-mass spectrometry. Carcinogens can be identified accurately using an appropriate method chosen from the available alternatives. The evaluations of detection methods presented in this review are based on advantages and disadvantages, accuracy, speed and time factor of methods and experimental procedures by previous scholars. This review contributes as guidance to readers on existing methods within different fields and their exclusive applications on different types of carcinogens in water.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".