Forensic analysis by solid sampling electrothermal vaporization coupled to inductively coupled plasma optical emission spectrometry
Bibliographic record
Abstract
To analyze trace evidence found at crime scenes, non-destructive analysis techniques or analysis techniques requiring minute amounts of sample are preferred. One such technique is solid sampling electrothermal vaporization (ETV) coupled to inductively coupled plasma optical emission spectrometry (ICPOES), which requires only 0.1-5 mg of sample. As a result, it has been utilized in several applications of forensic research. This article discusses the capabilities of ETV-ICPOES among current analytical methods and introduces its value as a tool for the analysis of forensic evidence. The latest advancements of ETV-ICPOES demonstrate the diverse opportunities for the identification, determination, and discrimination of evidence. Methods of ETV-ICPOES for the direct analysis of various physical evidence, including trace evidence, are reviewed. Some methods involve quantification of multiple elements using, commonly, matrix-matched external calibration with certified reference materials. Other methods combine qualitative multi-element analysis, based on the area of each analyte peak produced during the vaporization step of the ETV temperature program, and multivariate analysis using principal component analysis or linear discriminant analysis. In all cases, internal standardization with an argon emission line first compensates for sample loading effects on the plasma. Perspectives for utilizing ETV-ICPOES in future forensic settings are also offered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".