Atomic spectrometry update. Industrial analysis: metals, chemicals and advanced materials
Bibliographic record
Abstract
This is the latest review covering atomic spectrometric measurements of industrial materials, metals, chemicals and advanced materials. It follows on from last year’s review and should be read in conjunction with other reviews in the series. Some areas covered in this review are expanding rapidly, with substantially more research interest than had been noted previously. There has therefore been a re-organisation of the structure of the review. Nanotechnology and the analysis of thin films have been split into separate sections. There is also an effort to be more critical of the published literature and to direct the reader towards areas of expanding research. In an attempt to make the review into an easy reference source, the use of tables has been re-introduced. Literature cited in the tables, although interesting is, in the opinion of the authors, of lesser importance to the atomic spectrometric analyst than that discussed in the text. There is also increasing overlap of subject areas, e.g., with many ceramics being used in electronic capacitors. Other areas may also overlap, e.g. glass and ceramics and even ceramics and metals (CERMETS). It is therefore worth noting that some papers may be discussed in more than one section of the review and that other papers may only be discussed once, but in an unexpected place. The writing team are keen to elicit feedback from readers of this review and invite you to complete the Atomic Spectroscopy Updates questionnaire on http://www.asureviews.org
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.036 |
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".