Ultrahigh‐resolution mass spectrometry of simulated runoff from treated oil sands mature fine tailings
Bibliographic record
Abstract
There is interest in using mature fine tailings (MFT) in reclamation strategies of oil sands mining operations. However, simulated runoff from different dried MFT treatments is known to have elevated levels of salts, toxic ions, and naphthenic acids, and alkaline pH and it is phytotoxic to the emergent macrophyte, common reed (Phragmites australis). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) of the acidic species in the runoff confirmed that the distribution of oil sands naphthenic acids and associated oil sand acids was dependent on the MFT treatment. Furthermore, FT-ICR MS studies of the acidic species in hydroponic systems revealed that there was no plant-mediated change in the electrospray ionization mass spectra of the runoff. O(o)-containing species were prevalent (>90%), O(o)S(s) were predominant (<10% relative abundance), and O(o)N(n) were least abundant in all runoff water samples. O(o)S(s) species were predominant in all the samples investigated. The heteroatomic classes present in runoff water at greater than 1% relative abundance include: O(2)N(1), O(3)N(1), O(2), O(2)S(1) O(3), O(3)S(1), O(4), O(4)S(1), O(5), O(5)S(1), O(6), O(6)S(1), O(7), O(7)S(1), O(8) and O(8)S(1). Assuming the same response factor for all O(o) species, the O(4) class, presumably dicarboxylic acids, was generally more prevalent than the O(2) class in all samples. The O(2) class is indicative of classical naphthenic acids. However, dicarboxylic acids will form negative ions more readily than the monocarboxylic acids as there are two acidic hydrogens available for formation of these 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 | 0.000 | 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 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".