Profiling Oil Sands Mixtures from Industrial Developments and Natural Groundwaters for Source Identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study was to identify chemical components that could distinguish chemical mixtures in oil sands process-affected water (OSPW) that had potentially migrated to groundwater in the oil sands development area of northern Alberta, Canada. In the first part of the study, OSPW samples from two different tailings ponds and a broad range of natural groundwater samples were assessed with historically employed techniques as Level-1 analyses, including geochemistry, total concentrations of naphthenic acids (NAs) and synchronous fluorescence spectroscopy (SFS). While these analyses did not allow for reliable source differentiation, they did identify samples containing significant concentrations of oil sands acid-extractable organics (AEOs). In applying Level-2 profiling analyses using electrospray ionization high resolution mass spectrometry (ESI-HRMS) and comprehensive multidimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOF/MS) to samples containing appreciable AEO concentrations, differentiation of natural from OSPW sources was apparent through measurements of O2:O4 ion class ratios (ESI-HRMS) and diagnostic ions for two families of suspected monoaromatic acids (GC × GC-TOF/MS). The resemblance between the AEO profiles from OSPW and from 6 groundwater samples adjacent to two tailings ponds implies a common source, supporting the use of these complimentary analyses for source identification. These samples included two of upward flowing groundwater collected <1 m beneath the Athabasca River, suggesting OSPW-affected groundwater is reaching the river system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it