Tiered approach to long-term weathered lubricating oil analysis: GC/FID, GC/MS diagnostic ratios, and multivariate statistics
Bibliographic record
Abstract
Frequent small-scale environmental releases of lubricating (lube) oils have deleterious effects on aquatic ecosystems. In the event of a spill, oil fingerprinting is critical to source attribution, clean-up procedures, and liability assignment. Oil forensic investigations are particularly challenging when oils are weathered over an extended period of time, as a large number of biomarkers become lost and the chemical composition changes significantly from its source. This study simulated an environmental case in which long-term weathered lube oil "spill" samples were matched to unweathered suspect "source" oils. While traditional oil fingerprinting techniques including GC/FID and GC/MS diagnostic ratios were insufficient for reliably attributing the source, a comprehensive and systematically tiered approach proved successful. The proposed methodology featured three tiers: Tier 1 GC/FID, Tier 2 GC/MS diagnostic ratios, and Tier 3 multivariate statistics. This novel approach provided environmental chemists with a powerful tool for dealing with an otherwise extremely challenging lube oil forensic investigation.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".