Chapter 4: Simulated Distillation Measurement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| 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) | 0.002 | 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 it