Enhanced characterization of oil sands acid-extractable organics fractions using electrospray ionization–high-resolution mass spectrometry and synchronous fluorescence spectroscopy
Bibliographic record
Abstract
The open pit oil sands mining operations north of Fort McMurray, Alberta, Canada, are accumulating tailings waste at a rate approximately equal to 4.9 million m(3) /d. Naphthenic acids are among the most toxic components within tailings to aquatic life, but structural components have largely remained unidentified. In the present study, electrospray ionization high-resolution mass spectrometry (ESI-HRMS) and synchronous fluorescence spectroscopy (SFS) were used to characterize fractions derived from the distillation of an acid-extractable organics (AEO) mixture isolated from oil sands process-affected water (OSPW). Mean molecular weights of each fraction, and their relative proportions to the whole AEO extract, were as follows: fraction 1: 237 Da, 8.3%; fraction 2: 240 Da, 23.8%; fraction 3: 257 Da, 26.7%; fraction 4: 308 Da, 18.9%; fraction 5: 355 Da, 10.0%. With increasing mean molecular weight of the AEO fractions, a concurrent increase occurred in the relative abundance of nitrogen-, sulfur-, and oxygen-containing ions, double-bond equivalents, and degree of aromaticity. Structures present in the higher-molecular-weight fractions (fraction 4 and fraction 5) suggested the presence of heteroatoms, dicarboxyl and dihydroxy groups, and organic acid compounds with the potential to function as estrogens. Because organic acid compositions become dominated by more recalcitrant, higher-molecular-weight acids during natural degradation, these findings are important in the context of oil sands tailings pond water remediation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".