Impact of sample preparation on bitumen content measurement using laser-induced breakdown spectroscopy
Bibliographic record
Abstract
The impact of sample preparation on bitumen content measurement using LIBS was investigated by collecting spectra from wet and dry tailings. A multivariate data analysis model was developed using optimal wavelength selection for bitumen content classification and prediction in tailings. Wet tailings can be classified into three classes (low, medium, and high bitumen) with 12.1 % error, while dry tailings have a classification error of 6.1 %. Quantitative analysis showed a bitumen content prediction error of 4.7 % for wet tailings and 8.9 % for dry tailings. Wet tailings showed a 1.8–2.5 times improvement in the limit of detection range compared to dry tailings. Plasma density and crater size measurements revealed that plasma density fluctuation was 2.7 times lower in wet tailings due to consistent crater formation from laser-tailings interaction. The lower plasma density fluctuation indicates a stable mass ablation for wet samples, which is attributed as the primary reason for significant LIBS performance improvement on wet tailings. • Optimization of multivariate calibration model for improved prediction accuracy of the bitumen measurement in using LIBS. • Bitumen measurement accuracy significantly improved in wet tailings compared to dry tailings. • Plasma density and laser-produced craters are substantially consistent for wet tailings. • Use of compact laser and developed calibration model are promising for the future advancement of portable LIBS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".