Machine Learning in the Context of<scp>Laser‐Induced</scp>Breakdown Spectroscopy
Bibliographic record
Abstract
This chapter presents the fundamental ideas behind the most common machine-learning (ML) techniques found in the laser-induced breakdown spectroscopy literature. It describes random forests, support vector machines, artificial neural networks, unsupervised learning, and self-organizing maps. The chapter begins, for historical reasons, with one of the first ML algorithms – decision trees. Using the concept of decision trees, it then conceptually introduces several ensemble methods, i.e., methods that combine or aggregate the results of multiple simple models to form a more powerful prediction. Namely, the chapter discusses bootstrap aggregation (bagging), boosting and its more powerful variant, gradient boosting, and lastly, random forests. It emphasizes that while the ensemble methods are described considering decision trees, they are not limited to trees. Thus, the presented ensembling methods can be applied to improve the performance of any other ML model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".