Mass spectrometric characterization of naphthenic acids in environmental samples: A review
Bibliographic record
Abstract
There is a growing need to develop mass spectrometric methods for the characterization of oil sands naphthenic acids (structural formulae described by C(n)H(2n+z)O(2) where n is the number of carbon atoms and "z" is referred to as the "hydrogen deficiency" and is equal to zero, or is a negative, even integer) present in environmental samples. This interest stems from the need to better understand their contribution to the total acid number of oil sands acids; along with assessing their toxicity in aquatic environments. Negative-ion electrospray ionization has emerged as the analytical technique of choice. For infusion samples, matrix effects are particularly evident for quantification in the presence of salts and co-elutants. However, such effects can be minimized for methods that employ chromatographic separation prior to mass spectrometry (MS) detection. There have been several advances for accurate identification of classes of naphthenic acid components that employ a range of MS hyphenated techniques. General trends measured for degradation of the NAs in the environment appear to be similar to those obtained with either low- or high-resolution MS. Future MS research will likely focus on (i) development of more reliable quantitative methods that use chromatography and internal standards, (ii) the utility of representative model naphthenic acids as surrogates for the complex NA mixtures, and (iii) development of congener-specific analysis of the principal toxic components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".