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
One of the most common compositional analyses for petroleum samples is known as the SARA (saturates, aromatics, resins, and asphaltenes) fractionation test. SARA fractionation is also used as one of the screening criteria for asphaltene stability of reservoir fluids due to pressure depletion or commingling of different fluids. There are numerous variations of this type of analysis. First, the extraction of asphaltenes is not consistent from method to method. Asphaltenes are extracted using either pentane, hexane, or heptane. There are no specific reasons for selecting one over the other, and usually the users do not associate differences in results with the nature of the solvent. In addition, the extraction temperature could have an impact on the amounts of asphaltenes extracted. The fractionation of maltenes is also a challenge, usually ignored by end-users. Assuring no overlap between fractions and obtaining a very good mass balance are among these challenges. They could be impacted by the type of packing material amount of solvents used for the chromatographic separation. These SARA methods, referred to as standard methods, usually generate different results leading to confusion if the users are not that familiar with analytical details of each method. This paper discusses the role of the major parameters involved in generating the four fractions and how these parameters affect results, thus impacting decision for the end-users. It also shows that it is impossible to perform any prediction of results when changing from one method to another.
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) | 0.000 | 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