Additional file 1 of Rapid spilled oil analysis using direct analysis in real time time-of-flight mass spectrometry
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,995 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».