A Perspective of the Comprehensive and Objective Assessment of Analytical Methods Including the Greenness and Functionality Criteria: Application to the Determination of Zinc in Aqueous Samples
Bibliographic record
Abstract
The recently proposed concept of White Analytical Chemistry (WAC), referring to the Red-Green-Blue color model, combines ecological aspects (green) with functionality (red and blue criteria), presenting the complete method as "white". However, it is not easy to carry out an overall quantitative evaluation of the analytical method in line with the WAC idea in an objective manner. This paper outlines the perspective of the future development of such a possibility by attempting to answer selected questions about the evaluation process. Based on the study consisting in the evaluation of selected model methods by a group of 12 independent analysts, it was shown how well individual criteria are assessed, whether the variability of assessments by different people is comparable for each criterion, how large it is, and whether averaging the scores from different researchers can help to choose the best method more objectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".