SOURCE IDENTIFICATION OF AN UNKNOWN SPILL (2002) FROM QUEBEC BY THE MULTI-CRITERION ANALYTICAL APPROACH AND LAB SIMULATION OF THE SPILL SAMPLE
Bibliographic record
Abstract
ABSTRACT This paper describes a case study of an oil spill into Canal Ste-Anne-de-Bellevue, Quebec in 2002. In response to this specific oil identification need, a lab spill simulation was designed to obtain simulated spill samples from the suspected source samples. The integrated multi-criterion approach using GC/MS and GC/FID was then applied for fingerprinting and identifying the spill oil samples. The distribution patterns of hydrocarbons in the spill and suspected source samples were recognized and compared. Analysis of oil-characteristic biomarkers and the extended suite of parent and alkylated polycyclic aromatic hydrocarbons (PAH) were performed. A variety of diagnostic ratios of “source-specific marker” compounds for interpreting chemical fingerprinting data were determined and analyzed. Finally, the major components in suspected source samples were identified. The detailed chemical characterization data highlight that: (1) the spilled oil 264 is diesel fuel, while the suspected source sample 265 is an emulsified Bunker C type fuel; (2) another suspected source sample 266 is a de greaser-type “pine oil” product; (3) the synthetic spill sample from 265 and 266 has a completely different GC profile and chemical composition from the spill sample 264. No component of sample 266 was found and recognized in sample 264.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".