Improved and Standardized Methodology for Oil Spill Fingerprinting
Bibliographic record
Abstract
The existing Nordtest methodology for oil spill Identification has over the past 10 years formed an important “platform” for solving oil spill identification cases both in the Scandinavian countries as well as other countries in Europe, the USA and Canada. “Revision of the Nordtest Methodology for Oil Spill Identification” is a cooperative project between the National Oil Spill Identification laboratories in Norway, Sweden, Finland, Denmark and the Battelle Memorial Institute (Duxbury) in the USA. The goals of the project are: (1) to refine the existing Nordtest methodology into a technically more robust and defensible oil spill identification methodology with focus on determination of quantitative diagnostic indices (ratios) and (2) to adjust the revised Nordtest methodology into guidelines for the European Committee for Standardization (CEN). This paper presents the recommended methodology for the analytical oil spill identification part. The sampling techniques and handling of oil samples and background (reference) samples prior to their arrival at the environmental forensic laboratory is not covered in this paper. The recommended methodology approach is a result of documented analytical improvements and a more quantitative treatment of analytical data from gas chromatographic-flame ionization detector (GC/FID) and gas chromatographic-mass spectrometer methods (GC/MS-SIM) and the operational experiences over past few years among the participating forensic laboratories. The experience and literature in the field of oil exploration and production geochemistry have also played an important role for the recommended methodology. The results from a recent Round Robin test carried out among 12 laboratories using this new methodology are presented in a separate paper in this issue (8).
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".