Low adsorption affinity of athabasca oil sands naphthenic acid fraction compounds to a peat-mineral mixture
Bibliographic record
Abstract
Much of the toxicity in oil sands process-affected water in Athabasca oil sands tailings has been attributed to naphthenic acids (NAs) and associated naphthenic acid fraction compounds (NAFCs). Previous work has characterized the environmental behaviour and fate of these compounds, particularly in the context of constructed treatment wetlands. There is evidence that wetlands can attenuate NAFCs in natural and engineered contexts, but relative contributions of chemical, biotic, and physical adsorption with sequestration require deconvolution. In this work, the objective was to evaluate the extent to which prospective wetland substrate material may adsorb NAFCs using a peat-mineral mix (PMM) sourced from the Athabasca Oil Sands Region (AOSR). The PMM and NAFCs were first mixed and then equilibrated across a range of NAFC concentrations (5–500 mg/L) with moderate ionic strength and hardness (∼200 ppm combined Ca2+ and Mg2+) that approximate wetland water chemistry. Under these experimental conditions, low sorption of NAFCs to PMM was observed, where sorbed concentrations of NAFCs were approximately zero mg/kg at equilibrium. When NAFCs and PMM were mixed and equilibrated together at environmentally relevant concentrations, formula diversity increased more than could be explained by combining constituent spectra. The TOC present in this PMM was largely cellulose-derived, with low levels of thermally recalcitrant carbon (e.g., lignin, black carbon). The apparent enhancement of the concentration and diversity of components in PMM/NAFCs mixtures are likely related to aqueous solubility of some PMM-derived organic materials, as post-hoc combination of dissolved components from PMM and NAFCs cannot replicate enhanced complexity observed when the two components are agitated and equilibrated together.
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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".