Non‐target profiling of bitumen‐influenced waters for the identification of tracers unique to oil sands processed‐affected water (OSPW) in the Athabasca watershed of Alberta, Canada
Bibliographic record
Abstract
Rationale The objective of this study was to identify unique chemical tracers of oil sands process‐affected water (OSPW) to enable definitive discrimination of tailings pond seepage from natural bitumen‐influenced waters from the Canadian Alberta McMurray formation. Methods The approach involved comparing unknowns from an unprecedented sample set of OSPW ( n = 4) and OSPW‐affected groundwaters ( n = 15) with natural bitumen‐influenced groundwaters ( n = 20), using high‐performance liquid chromatography/electrospray ionisation high‐resolution mass spectrometry (HPLC/ESI‐HRMS) operated in both polarities. Results Four unknown chemical entities were identified as potential tracers of OSPW seepage and subsequently subjected to structural elucidation. One potential tracer, tentatively identified as a thiophene‐containing carboxylic acid [C 15 H 23 O 3 S] − , was only detected in OSPW and OSPW‐affected samples, thereby showing the greatest diagnostic potential. The remaining three unknowns, postulated to be two thiochroman isomers [C 17 H 25 O 3 S] + and an ethyl‐naphthalene isomer [C 16 H 21 ] + , were detected in one and two background groundwaters, respectively. Conclusions We advanced the state of knowledge for tracers of tailings seepage beyond heteroatomic classes, to identifying diagnostic substances, with structures postulated. Synthesis of the four proposed structures is recommended to enable structural confirmations. This research will guide and inform the Oil Sands Monitoring Program in its efforts to assess potential influences of oil sands development on the Athabasca River watershed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".