Atomic spectrometry update. Industrial analysis: metals, chemicals and advanced materials
Bibliographic record
Abstract
This review period has shown several areas of growth. The increase in popularity of LIBS continues as the problems, both real and perceived, that are associated with it (e.g., that it is capable only of qualitative analyses) are overcome. The area that appears to have seen the greatest increase in LIBS work is the nuclear industry. Presumably this is because of the stand-off ability of the technique. Another technique that is increasing in popularity is continuum source AAS. This has found substantial use in several areas of the review, notably the fuels and the organic chemicals sections. As noted in the review, the technique allows similar multi-elemental detection to ICP-OES (albeit at lower sensitivity), but at AAS running costs and is therefore likely to remain a popular technique. The necessity of causing no or minimal damage to forensic samples and for samples of archaeological or historical importance is still paramount. Therefore, micro-sampling techniques such as LIBS, LA and various X-ray-based techniques are still popular. Since the reliability of the data obtained from hand-held/portable XRF instruments has improved significantly in recent times, the use of these can be regarded as almost routine. Also noted in the review is the propensity for using multiple techniques, often simultaneously, to characterize materials more fully and more rapidly. This is the latest review covering atomic spectrometric measurements of industrial materials, metals, chemicals and advanced materials. It follows on from last year's review1 and should be read in conjunction with other reviews in the series.2–5 This year has seen the departure of Sian Shore from the writing team. Her efforts over the last few years have been very much appreciated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".