A study of Asphalt Binders by X-Ray Diffraction \nUsing Pearson-VII, Pseudo-Voigt and Generalized \nFermi Functions
Bibliographic record
Abstract
Twenty-three asphalt binder samples were obtained from Northern Ontario, Alberta \nfrom Canada, Montana USA, and Venezuela. Structural studies of asphalt in crude oil \nhave been performed by X-ray diffraction (XRD). The XRD spectra were taken with \na Rigaku Dmax 2200V/PC, and Jadeᵀᴹ software was used for initial analysis. XRD \nimplementation with Cu-K- radiation operating at 40 KV and 40 mA, with a scan rate \nof 0.001ﾟ 2 θ per second. The XRD data were fitted with (Pearson VII, pseudo-Voigt) \nprofiles, and then modeled in Mathematica using a generalized Fermi function (GFF). \nThe results are discussed in terms of their accuracy with different combinations \nof backgrounds such as (Linear, Level, Fixed, Parabolic, 3ᴿᴰ order Polynomial, 4ᵀᴴ \norder Polynomial). In addition, the fits also include various parameters, for example, \nThreshold Sigma, Intensity Cutoff, Range to find background, Exponent, Skewness \nand Lorentzian.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".