Diesel Fuel Ignition Quality as Determined in the Ignition Quality Tester (IQT™) - Part IV
Bibliographic record
Abstract
This paper reports on the fourth part of a continued study on further research and development with the automated Ignition Quality Tester (IQT™). Research over the past six years (reported in SAE papers #961182, 971636 and 1999-01-3591) has demonstrated the capabilities of this automated apparatus to measure the ignition quality and accurately determine a derived cetane number (DCN) for a wide range of middle distillate and non-conventional diesel fuels. The present paper reports on a number of separate investigations supporting these continued studies. These investigations include: (1) the development/validation of critical calibration, operation and testing procedures, (2) the development of component/parameter specifications, operating limits, etc., and (3) the assessment of the performance of the IQT™ for a wide variety of diesel fuels, when operating in accordance with the Institute of Petroleum (IP) test method (in the 2001 year book as a draft test method) and the American Society for Testing and Materials (ASTM) draft test method. Performance capabilities were also evaluated in terms of cetane improver additive and blending component response. This paper also presents initial insights into the precision capability of the IQT™ and associated test methods, based on the results of two preliminary round robin studies. On-going co-operative efforts, aimed at furthering the standard development processes in both Europe and North America, are also 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".