Fuel correlations for combustion purposes: a summary of progress within the past fifteen years. II
Bibliographic record
Abstract
For pt.I see ibid., p.1968-74 (1996). Over the years, many correlations for fuel properties have been developed at Laval University. The main goal in this was to provide tools to estimate unknown fuel properties from the known values of the density and the viscosity at one temperature and the ASTM D-86 distillation, since these data are easily determined. The first part of the this paper dealt with properties of liquid fuels. Most of the correlations in this second part are not so much pertinent to fuel properties per se, but rather to lean premixed flame behavior. Much of the behavior is related to two postulated temperatures, T/sub auig/ and T/sub i/. The first (the auto ignition temperature) may be regarded as the 'start' of a reaction and the second (the instantaneous, spontaneous ignition temperature) may be regarded as that temperature at which the reaction first becomes self-sustaining. Between them they account for a good deal of premixed combustion behavior.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".