Additional file 1 of Rapid spilled oil analysis using direct analysis in real time time-of-flight mass spectrometry
Bibliographic record
Abstract
Additional file 1: Table S1. Specific details for each oil used for heat map building. Table S2. Excel documents used for Exploratory Search of Biomarker Class Compounds and Lubricant Additives. Table S3. Ions used to construct Principal Component Analysis and Discriminant Analysis of Principal Component. Table S4. Discriminant Analysis of Principal Components External Validation Scores. Table S5. Discriminant Analysis of Principal Components classifications of QAs into lubricating oil, crude oil/diluted bitumen, heavy fuel oil/intermediate fuel oil and diesel/jet fuel.Table S6. Final Oil typing results. Figure S1. Spectra of QSPP (Lubricating oil). Figure S2. Spectra of MD (Diesel). Figure S3. Spectra of JET A1 (Jet Fuel). Figure S4. Spectra of IFO-180 (Intermediate Fuel Oil). Figure S5. Spectra of WCS (Crude Oil/Bitumen). Figure S6. Spectra of HFO6303 (Heavy Fuel Oil). Figure S7. Spectra of AWB (Crude Oil/ Diluted Bitumen). Figure S8. Spectra of PVG (Lubricating Oil). Figure S9. Spectra of UNI (Lubricating Oil). Figure S10. Spectra of QSPP with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S11. Spectra of MD with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S12. Spectra of IFO with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S13. Spectra of WCS with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S14. Spectra of HFO with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S15. Spectra of AWB with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S16. Spectra of PVG with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S17. Spectra of UNI with identified compound classes highlighted in red. Relative abundances of all extracted compounds are listed. Figure S18. Intermediate Fuel Oil Heatmap. Figure S19. Crude Oil Heatmap. Figure S20. Jet Fuel Oil Heatmap. Figure S21. Lubricating Oil Heatmap. Figure S22. Diesel Heatmap. Figure S23. Heavy Fuel Oil Heatmap. Figure S24. Diluted Bitumen Heatmap. Figure S25. Three dimensional PCA plot for classes: Diesel/Jet, Lube, Crude/Dilbit and HFO/IFO. Figure S26. Two-dimensional discriminant analysis of principal components plot for classes: Diesel/Jet, Lube, Crude/Dilbit and HFO/IFO. Figure S27. PCA plot of dilbit and crude reference data. Figure S28. PCA plot of HFO and IFO reference data. Figure S29. DAPC plot of dilbit and crude oil reference data. Figure S30. DAPC plot of jet fuel and diesel reference data. Figure S31. DAPC plot of heavy fuel oil and intermediate fuel oil reference data. Figure S32. Positive ion heat map of QA1 compared to intermediate fuel oil and heavy fuel oil reference data. Figure S33. Positive ion heat map of QA2 compared to crude oil and diluted bitumen reference data. Figure S34. Positive ion heat map of QA3 compared to crude oil and diluted bitumen reference data. Figure S35. PCA of QA1 compared to intermediate fuel oil and heavy fuel oil reference data. Figure S36. PCA of QA1 compared to intermediate fuel oil and heavy fuel oil reference data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.995 | 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".