Gas Chromatographic Simulated Distillation-Mass Spectrometry for the Determination of the Boiling Point Distributions of Crude Oils
Bibliographic record
Abstract
The coupling of gas chromatographic simulated distillation with mass spectrometry for the determination of the distillation profiles of crude oils is reported. The method provides the boiling point distributions of both weight and volume percent amounts. The weight percent distribution is obtained from the measured total ion current signal. The total ion current signal is converted to weight percent amount by calibration with a reference crude oil of a known distillation profile. Knowledge of the chemical composition of the crude oil across the boiling range permits the determination of the volume percent distribution. The long-term repeatability is equivalent to or better than the short-term repeatability of the currently available American Society for Testing and Materials (ASTM) gas chromatographic method for simulated distillation. Results obtained by the mass spectrometric method are in very good agreement with results obtained by conventional methods of physical distillation. The compositional information supplied by the method can be used to extensively characterize crude oils.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".