Green analytical chemistry-a new Elsevier's journal facing the realities of modern analytical chemistry and more sustainable future
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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