Characterization and Comparison of Dissolved Organic Matter Signatures in Steam-Assisted Gravity Drainage Process Water Samples from Athabasca Oil Sands
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) process water contains high concentrations of dissolved organic and inorganic matter. A wide range of analytical techniques including electrospray ionization mass spectrometry, gas chromatography–mass spectrometry, Fourier transform infrared spectrometry, and fluorescence spectrophotometry have been utilized for the identification and measurement of dissolved organic matter (DOM) in oil sands process-affected water. The composition of DOM in the SAGD water is relatively complex, and thus one plausible method for its analysis is the fractionation of DOM into hydrophilic and hydrophobic portions using suitable resin columns and the characterization of these fractions using standard analytical methods. Comparing the fractionation and characterization of the SAGD produced water from different plant sites can provide considerable insight into better management, recycle, and reuse of this process water. Also, a detailed knowledge of the chemical composition of the SAGD produced water provides guidelines for identifying the constituents that are responsible for scaling and fouling at various stages of the SAGD process. This study aims at developing a systematic approach for the fractionation and characterization methods of SAGD process water samples.
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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 | 0.000 | 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".