Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Very Similar Chemical Composition
Bibliographic record
Abstract
This paper describes a case study in which a multi-criterion approach was used to fingerprinting and identifying mystery oil samples. Three unknown oil samples were received from Quebec on March 28, 2001 for chemical analysis. The main purpose of this analysis was to determine the nature and the type of the products, detailed hydrocarbon composition of the samples, and whether these samples came from the same source. The samples were analyzed by gas chromatography with a flame ionization detector (GC-FID) and by gas chromatography coupled with mass spectrometry (GC-MS). Hydrocarbon distribution patterns of unknown oils were recognized. Multiple suites of analytes were quantified and compared. A variety of diagnostic ratios of “source-specific marker” compounds for interpreting chemical data were further determined and analyzed. The chemical fingerprinting results reveal the following: (1) These three oils are most likely a hydraulic-fluid type oil. (2) These three oils are very “pure”, largely composed of saturated hydrocarbons with the total aromatics being only 4–10% of the TPH. (3) The oils are a mixture of two different hydraulic fluids. There is no clear sign indicating they had been weathered. (4) The PAH concentrations are extremely low (<10 μg/g oil) in the oil samples, while the biomarker concentration are unusually high (4700–5500 μg/g oil). (5) Three major unknown compounds in the oil samples were positively identified. They are antioxidant compounds added to oils. (6) Samples 2996 and 2997 are identical and come from the same source. (7) The sample 2998 has group hydrocarbon compositions (including the GC traces, TPH, and total saturates) very similar to samples 2996 and 2997. But, it is not identical in chemical composition to samples 2996 and 2997, and they do not come from the same source.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".