APPLICATION OF STATISTICAL ANALYSIS IN THE SELECTION OF DIAGNOSTIC RATIOS FOR FORENSIC IDENTIFICATION OF AN OIL SPILL SOURCE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it