Evaluation of the Performance of Cryogen-Free Thermal Modulation-Based Comprehensive Two-Dimensional Gas Chromatography-Time-of-Flight Mass Spectrometry (GC×GC-TOFMS) for the Qualitative Analysis of a Complex Bitumen Sample
Bibliographic record
Abstract
Historically, one-dimensional gas chromatography combined with mass spectrometry (GC/MS) has been employed in the analysis of petrochemical samples such as diesel, crude oil and bitumen. With increasingly complex samples, obtaining detailed information can be difficult with this method due to the large number of coelutions. By implementing comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS), the limitations of GC/MS can be overcome, due to the ability of this method to separate mixtures using two different separation mechanisms and obtain full mass spectra. Furthermore, this enables an investigation of biomarkers, compounds which aid in the identification of geological and environmental processes, potentially differentiating crude oil samples. Cryogenic-based thermal modulators are typically used for this application due to their superior focusing effect; however, some platforms require expensive cryogenic consumables. The solid-state modulator (SSM), a cryogen-free thermal platform, was employed for the first time for the group and biomarker analysis of Alberta oil sands bitumen. Evaluation of the SSM performance was based on published literature data on bitumen analysis. Extracted ion chromatograms (EIC) and molecular ion peaks were used for the confirmation of the groups’ and individual’s analytes. Identification of the characteristic biomarkers responsible for determining thermal maturity, source rock or oil origin was achieved. These results indicate the successful analysis of bitumen by consumable-free, solid-state modulation-based GC×GC-TOFMS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".