Identification of sources and extent of weathering of tar-balls from the eastern seaboard of peninsular malaysia using hopanes and polycyclic aromatic hydrocarbons as molecular marker
Bibliographic record
Abstract
Oil pollution is considered to be one of the major contributors to marine pollution. The threat that oil pollution poses to the marine environment is extremely dangerous to its ecosystem. The South China Sea region is blessed with crude oil and has a proven oil reserves. Leaks and contaminations by oil fields are usually contributing factor to oil pollution in the region. However other major contributing factors like tanker accidents and ballast water is also substantial. Once oil is spilled to the ocean, the oil will go through many physical and biological processes like evaporation, emulsification, dissolution and microbial degradation; these initial processes will soon change the physical shape and chemical composition of the oil slick. Tar-balls are generated when emulsification occur on an oil slick, the very last stage of weathering. Tar-balls therefore are considered to be the remnants of an oil spill. These tar-balls will travel the oceans and end up on beaches. This study utilizes diagnostic ratios of n-alkanes, hopanes and polycyclic aromatic hydrocarbons (PAHs) to determine he origins, distribution and weathering of tar-balls. Hopanes ratios (e.g. C29/C30, and ΣC31 – C35/C30 ratios) were used to identify the origin of tar-balls. The weathering effects were distinguished by using alkanes, namely the Unresolved Complex Mixture (UCM) and low molecular weight/ high molecular weight (L/H) ratios. Similarly, PAHs were also used for the determination of weathering processes undergone by the tar-balls. These diagnostic ratios gave a very strong indication on the origins of tar-balls in this study. For example, 16 out of 17 samples originate from South East Asian Crude Oil (SEACO) with one sample from Merang, Terengganu originating from the North Sea Oil (Troll). The TRME-2 sample may have come from a supertanker’s ballast water discharge. The second possibility is that the source may have been transported via oceanography. The approaches applied in this study have given more insights on the behavior and weathering of the tar-balls in the marine environment.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".