Diagnostic Ratio Analysis: A New Concept for the Tracking of Oil Sands Process-Affected Water Naphthenic Acids and Other Water-Soluble Organics in Surface Waters
Bibliographic record
Abstract
A diagnostic ratio forensics tool, similar to that recognized internationally for oil spill source identification, is proposed for use in conjunction with existing LC/QToF quantitative methodology for bitumen-derived water-soluble organics (WSOs). The concept recognizes that bitumen WSOs bear a chemical skeletal relationship to stearane and hopane oil biomarkers. The method uses response ratios for 50 selected WSOs compared between samples by their relative percent difference and adopted acceptance criteria. Oil sands process-affected water (OSPW) samples from different locations within a single tailings pond were shown to match, while those from different industrial sites did not. Acid extractable organic samples collected over 3 weeks from the same location within a single tailings pond matched with each other; as did temporal OSPW samples a year apart. Blind quality assurance samples of OSPW diluted in surface waters were positively identified to their corresponding OSPW source. No interferences were observed from surface waters, and there was no match between bitumen-influenced groundwater and OSPW samples, as expected for different sources. Proof of concept for OSPW source identification using diagnostic ratios was demonstrated, with anticipated application in the tracking of OSPW plumes in surface receiving waters, together with the potential for confirmation of source.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".