Bibliographic record
Abstract
DISTILLATION IS THE MOST WIDELY USED SEPARAtion process in the petroleum industry. Knowledge of the boiling range of crude oils, refined fractions, and finished products has been an essential part of quality determination since the beginning of the refining industry. Routine laboratory scale physical distillation tests have been extensively used for determining the boiling ranges of crude feedstocks, distillation fractions, and a complete slate of refinery products (ASTM methods D86, D1160, D2892, and D5236) [1–4]. An alternative to physical distillation or true boiling point distillation is simulated distillation by gas chromatography. Eggerston et al. first described simulated distillation in 1960 [5]. Simulated distillation (SD) is equivalent to a 100 theoretical-plate physical distillation, is very rapid, reproducible, and easily automated, requires only a small microlitre sample volume, and can better define initial and final boiling points. Boiling range distribution data are used to evaluate new crude oils, to confirm crude quality, to monitor crude quality during transportation, and to provide information for the optimization of refinery processes. The ability to quickly and reliably evaluate crude oils and fractions has important economic implications. The full development of simulated distillation methods as routine procedures has had a significant impact on the ability to determine boiling point information for crude oils and petroleum products.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".