Fate and Persistence of Long-Term Spilled Metula Oil in the Marine Salt Marsh Environment: Degradation of Petroleum Biomarkers
Bibliographic record
Abstract
ABSTRACT Complete “total oil analyses” were performed on the 24-year-old spilled Metula oil samples collected during the 1998 field visit. Fingerprinting data clearly indicate, except those three samples collected from the East Marsh untreated plots (EE-GO, EE-CI and EE-C2) that were only moderately weathered, the spilled oil has undergone significant alteration in chemical composition after 24 years. There are no fundamental differences between heavily weathered and degraded West Marsh and treated East Marsh samples. However, the effect of the experimental tilling action has been to promote plant recolonization. This result indicates that the recolonization of the marshes almost certain would have been accelerated had tilling or mixing been conducted on these sites after the spill. Chemical analyses of the two asphalt pavement samples (WI-1 and WI-2) indicate extremely heavy degradation. For these two samples, complete spectrum of n-alkanes from C8 to C40 and greater than 98% of alkylated PAH homologues were lost, and even the highly biodegradation-resistant biomarker compounds were shown to be more or less altered. It is observed that biomarkers were generally degraded in the declining order of importance as follows: diasteranes > C27 steranes > tricyclic terpanes > pentacyclic terpanes > norhapanes ∼C29 αββ steranes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".