Bibliographic record
Abstract
ANOTHER PROPERTY CHARACTERISTIC OF PETRO leum products that is closely associated with distillation parameters is vapor pressure. More often than not, the performance of various petroleum products, especially those used in transportation fuel applications, are very much dependent on synergistic parameters involving distillation and vapor pressure data. As mentioned in Chapter 1, the measurement of vapor pressure characteristics of petroleum products began with ASTM D323-30T “Standard Test Method for Vapor Pressure of Natural Gasoline (Reid Method)” [1]. This test method has withstood the test of time and exists today essentially as the same test method originally published as a tentative method 75 years ago. Various test methods for measuring vapor pressure have come into use within the petroleum industry since that time. In 1991, a number of automatic test methods for vapor pressure measurement have gained approval in the industry, and technological advances in automatic vapor pressure measurements have dominated the market place, especially with the stringent requirements of regulations to protect the environment. This chapter will discuss the relevant details of the latest versions of the various vapor pressure measurement test methods currently available and in use in the petroleum industry. Details that would serve to provide a better understanding or clarification of the test methods will be discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.035 |
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".