APPLICATION OF STATISTICAL ANALYSIS IN THE SELECTION OF DIAGNOSTIC RATIOS FOR FORENSIC IDENTIFICATION OF AN OIL SPILL SOURCE
Bibliographic record
Abstract
ABSTRACT In this work, 14 fresh crude oils of different types and origins were analyzed by gas chromatography with mass-selective detection, and over 80 potentially diagnostic ratios were calculated based on the quantitation of isoprenoids, polycyclic aromatic hydrocarbons (PAHs), biomarkers, diamondoids, bicyclic sesquiterpanes and aromatic steranes, etc. Diagnostic power (DP) was calculated for the selection of the candidate source-sensitive diagnostic ratios and used to determine which ratios were most diagnostic among the crude oils studied. In order to investigate the effect of evaporative and biodegradative weathering on diagnostic ratios and thereby to differentiate weathering-resistant ratios from weathering-sensitive ratios, triplicate analyses were performed for two suites of reference oils, laboratory-evaporated Prudhoe Bay crude oils and laboratory-biodegraded Alberta Sweet Mixed Blend (ASMB) crude oils, respectively. Student'S t-test was used to statistically evaluate whether diagnostic ratios were significantly affected by weathering and to ensure that the observed change is not due to analytical variance. It was found that, diagnostic ratios generally remained consistent for oils with slight to medium evaporative weathering, only the ratios of those compounds with lower boiling points such as adamantanes changed greatly. For biodegraded oils, most of diagnostic ratios remained constant for lightly to moderately biodegraded oils; while most of diagnostic ratios with exception of certain triaromatic steranes and high-molecular-weight terpane and sterane biomarkers demonstrated significant changes for heavily biodegraded oils.
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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 | 0.034 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".