<i>n</i>-Alkane Profiles of Engine Lubricating Oil and Particulate Matter by Molecular Sieve Extraction
Bibliographic record
Abstract
As part of the Canadian Atmospheric Fine Particle Research Program to obtain reliable primary source emission profiles, a molecular sieve method was developed to reliably determine n-alkanes in lubricating oils, vehicle emissions, and mobile source dominated ambient particulate matter (PM). This work was also initiated to better calculate carbon preference index values (CPI: the ratio of the sums of odd over even n-alkanes), a parameter for estimating anthropogenic versus biogenic contributions in PM. n-Alkanes in lubricating oil and mobile source dominated PM are difficult to identify and quantify by gas chromatography due to the presence of similar components that cannot be fully resolved. This results in a hump, the unresolved complex mixture (UCM) that leads to incorrect n-alkane concentrations and CPI values. The sieve method yielded better chromatography, unambiguous identification of n-alkanes and allowed examination of differences between n-alkane profiles in light (LDV) and heavy duty vehicle (HDV) lubricating oils that would have been otherwise difficult. These profile differences made it possible to relate the LDV profile to that of the PM samples collected during a tunnel study in August 2001 near Vancouver (British Columbia, Canada). The n-alkane PM data revealed that longer sampling times result in a negative artifact, i.e., the desorption of the more volatile n-alkanes from the filters. Furthermore, the sieve procedure yielded n-alkane data that allowed calculation of accurate CPI values for lubricating oils and PM samples. Finally, this method may prove helpful in estimating the respective diesel and gasoline contributions to ambient PM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".