Chemometric Processing of <scp>LIBS</scp> Data
Bibliographic record
Abstract
Chemometrics coupled to laser-induced breakdown spectroscopy (LIBS) has led to very successful results for qualitative and quantitative analysis and showed a great benefit to the LIBS field. This chapter provides a global and up-to-date picture of the many multivariate approaches and methods, with an emphasis on the best practices in this field, for the benefit of the LIBS technique, which has become a key player in the realm of applied spectroscopy. It presents some of the multivariate methods of data observation, such as principal component analysis; quantification, such as partial least squares (PLS); and classification, such as PLS-discriminant analysis. These methods allow to understand the very relation between the analyte reference and the LIBS signal. The chapter also discusses the data preprocessing concept and methods, such as normalization and scaling, for the improvement of the model prediction.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".