Use of the distributions of adamantane acids to profile short-term temporal and pond-scale spatial variations in the composition of oil sands process-affected waters
Bibliographic record
Abstract
Oil industry produced waters, such as the oils sands process-affected waters (OSPW) of Alberta, Canada, represent a challenge in terms of risk assessment and reclamation due to their extreme complexity, particularly of the organic chemical constituents, including the naphthenic acids (NA). The identification of numerous NA in single samples has raised promise for the use of NA distributions for profiling OSPW. However, monitoring of the success of containment is still difficult, due to the lack of knowledge of the homogeneity (or otherwise) of OSPW composition within, and between, different industry containments. Here we used GC×GC-MS to compare the NA of five OSPW samples from each of two different industries. Short-term temporal and pond-scale spatial variations in the distributions of known adamantane acids and diacids and other unknown tricyclic acids were examined and a statistical appraisal of the replicate data made. The presence/absence of individual acids easily distinguished the OSPW NA of one industry from those of the other. The proportions of tricyclic acids with different carbon numbers also varied significantly between the OSPW of the two industries. The pond-scale spatial variation in NA in OSPW samples was higher than the short-term (2 weeks) temporal variations. An OSPW sample from an aged pond was exceptionally high in the proportion of C15,16,17 compounds, possibly due to increased biotransformation. Such techniques could possibly also help to distinguish different sources of NA in the environment.
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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 | 0.001 | 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 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".