Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Characterization of Treated Athabasca Oil Sands Processed Waters
Bibliographic record
Abstract
Ultrahigh-resolution negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize Athabasca oil sands processed water (OSPW) treated by (A) coagulation flocculent with lime and bentonite, (B) coagulation flocculent with lime and bentonite followed by activated carbon, and (C) combined ozonation and ultrasonication. Treatment A was ineffective in reducing the level of total naphthenic acid fraction components [NAFCs, defined as the acid-extractable fraction of OSPWs or crude oils (C n H 2 n + z O w S x N y ), where the values n, w, x, and y indicate the number of carbon, oxygen, sulfur, and nitrogen atoms, respectively, and z represents the hydrogen atom deficiency because of the presence of double bonds and ring formation]. Likewise, for treatment A, little or no change was observed for the double bond equivalent (DBE) distributions of the compound classes. Treatments B and C resulted in the reduction of total NAFCs by 26 ± 1.4 and 89 ± 1.1%, respectively. For the latter treatments, there was evidence for selective removal of the S and O x S y heteroatomic species at the molecular level, along with a reduction in the DBE values for all species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".