Detection\nof Residual Oil-Sand-Derived Organic Material\nin Developing Soils of Reclamation Sites by Ultra-High-Resolution\nMass Spectrometry
Bibliographic record
Abstract
The\nreconstruction of disturbed landscapes back to working ecosystems\nis an issue of increasing importance for the oil sand areas in Alberta,\nCanada. In this context, the fate of oil-sand-derived organic material\nin the tailings sands used for reclamation is of utmost environmental\nimportance. Here we use electrospray ionization Fourier transform\nion cyclotron resonance mass spectrometry of maltene fractions to\nidentify compositional variations over a complete oil sand mining\nand recultivation process chain. On the basis of bulk compound class\ndistributions and percentages of unique elemental compositions, we\nidentify specific compositional features that are related to the different\nsteps of the process chain. The double bond equivalent and carbon\nnumber distributions of the N<sub>1</sub> and S<sub>1</sub>O<sub>2</sub> classes are almost invariant along the process chain, despite a\nsignificant decrease in overall abundance. We thus suggest that these\noil-sand-derived components can be used as sensitive tracers of residual\nbitumen, even in soils from relatively old reclamation sites. The\npatterns of the O<sub>2</sub>, O<sub>3</sub>, and O<sub>4</sub> classes\nmay be applied to assess process-chain-related changes in organic\nmatter composition, including the formation of plant-derived soil\norganic matter on the reclamation sites. The N<sub>1</sub>O<sub>2</sub> species appear to be related to unidentified processes in the tailings\nponds but do not represent products of aerobic biodegradation of pyrrolic\nnitrogen compounds.
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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 | 0.001 | 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".