Green analytical chemistry-a new Elsevier's journal facing the realities of modern analytical chemistry and more sustainable future
Bibliographic record
Abstract
Nowadays, all sciences, including chemistry and chemical engineering, are developing very dynamically. This can be seen in the rapidly growing number of scientific publications and citations in almost every field. Analytical chemistry is no exception and the possibilities of modern analytical methods have never been so great. The developed technological and methodological solutions allow for the determination of analytes at lower and lower concentration levels, separation of more and more complex mixtures, achieving precision and accuracy previously unreachable, while requiring even smaller amounts of material, ensuring even better speed of analysis and simplicity of use. Regardless of the development of analytical and practical possibilities, an important trend currently observed in analytical chemistry is the desire to reduce the negative impact of newly developed methods on the environment and to increase their safety. This idea, known as "green analytical chemistry" [1], [2], [3], is vividly expressed as "greening" of the applied procedures, which, however, does not always go hand-in-hand with the pursuit of maximum in analytical and practical/economic effectiveness. Therefore, it is essential to find an appropriate balance that would be consistent with the idea of sustainable development. For that reason, to meet these expectations, Elsevier has launched a new journal-Green Analytical Chemistry (GREE(N)AC). Its main mission is to offer developers and users of new analytical methods an original platform for publishing analytical solutions and exchanging ideas, facing the realities of modern analytical chemistry and creating a more sustainable future.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.035 |
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".