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Record W3193479157 · doi:10.1038/s41598-021-96572-5

Hazard assessment of oil spills along the main shipping lane in the Red Sea

2021· article· en· W3193479157 on OpenAlexaff
H. V. R. Mittal, Sabique Langodan, Peng Zhan, Shihan Li, Omar Knio, Ibrahim Hoteit

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsDalhousie University
FundersSaudi AramcoKing Abdullah University of Science and Technology
KeywordsOil spillEnvironmental scienceMesoscale meteorologyHazardOceanographyStructural basinMeteorologyFisheryGeographyGeologyEnvironmental protectionEcology

Abstract

fetched live from OpenAlex

This study investigates the risk from oil spills along the main shipping lane in the Red Sea based upon oil spill model trajectories forced by the outputs of validated high resolution regional met-ocean data. Following the intra-annual variations in the met-ocean conditions, the results are presented by classifying the basin into three regions: northern, central and southern Red Sea. The maximum distance traveled by the slick is presented for 1, 2, 5, 10, 14 and 20 days after the commencement of a spill. Different measures of hazard assessment in terms of the concentration of beached oil alongside the corresponding probability maps are also analyzed. The volume fractions of beached, dispersed and evaporated oil, 20 days after the commencement of a spill are quantified. The Red Sea general circulation is characterized by rich mesoscale eddies, which appear to be the most prevailing dynamics in oil transport in the basin. Two case events are analyzed to closely examine the effects of the mesoscale circulations on the fate of spilled oil. The results of this study provide a comprehensive assessment of oil spill hazards in the Red Sea, stemming its main shipping lane and identifies the areas at high risk that require timely mitigation strategies.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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 designObservational
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

Citations28
Published2021
Admission routes1
Has abstractyes

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