Characterization of Heavy Distillation Cuts Using Fourier Transform Infrared Spectrometry: Proof of Concept
Bibliographic record
Abstract
Fourier transform infrared spectrometry (FTIR) spectra were measured for 16 distillation cuts obtained from two bitumens using a deep vacuum fractionation apparatus. Three regions of the spectra were deconvoluted into peaks each associated with a known type of vibration: (1) aliphatic C–H stretching in the 2800–3000 cm –1 region, (2) aliphatic C–H scissoring/symmetric deformation in the 1350–1400 cm –1 region, and (3) aromatic C–H out-of-plane bending in the 680–900 cm –1 region. The distribution of chemical structures in the oils were assessed, and preliminary correlations were identified between measured physical properties (density, atomic H/C ratio, and molecular weight) and the quantified peak areas obtained from the FTIR spectra. A preliminary method was proposed to generate physical property distribution data for crude oils based on distillation and FTIR data. The method predicted the density, atomic H/C ratio, and molecular weight of the distillation cuts, with average deviations less than 0.8, 1.4, and 16%, respectively. Note that the method was tested on the same cuts used to generate the correlations because there were insufficient data for an independent test.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".