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Record W15229939

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

2008· dissertation· en· W15229939 on OpenAlexvenueno aff
Kuhan Chandru

Bibliographic record

VenueThe Journal of Rheumatology · 2008
Typedissertation
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsHopanoidsWeatheringtar (computing)PollutionEnvironmental scienceEnvironmental chemistrySteraneOil sandsGeologyChemistryGeochemistryAsphaltSource rockGeographyEcologyPaleontologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.218
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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