Bibliometric Overview of Research on Tasteand Odor in Drinking Waterduring the 1980-2022
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
Due to the global climate change, water resources have been withdrawn and the pollutant loads have become concentrated, thus causing the taste and odor components of the waters to be felt globally, leading to an increase in research in this area. The goal of this study was to analyze the trends in research from 1980 to 2022 that concentrated on both the formation and removal of taste and odor components in surface water resources throughout the world. 965 papers were examined a systematic review and bibliometric analysis. The findings revealed a growth in research on taste and odor compounds as well as the popularity and applicability of novel purification techniques in addition to more traditional ways for removing these substances. Water Research was the journal with the highest impact in this area. The United States, China, Canada, Australia, and South Korea were the top 5 most productive nations. Studies on the speciation of taste and odor components are in the minority but demands for innovative treatment techniques such as advanced oxidation processes have been considered, and these compounds are an area of research with significant potential. This study can assist research with its worldwide findings on taste and odor compounds.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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