MétaCan
Menu
Back to cohort
Record W2247932469 · doi:10.2166/wqrjc.2015.018

Effects of chemical dispersant and seasonal conditions on the fate of spilled oil – modelling of a hypothetical spill near Saint John, NB

2015· article· en· W2247932469 on OpenAlexafffund
Haibo Niu, Pu Li, Rujun Yang, Yongsheng Wu, Kenneth Lee

Bibliographic record

VenueWater Quality Research Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaDalhousie University
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaMarine Environmental Observation Prediction and Response Network
KeywordsDispersantPetroleumEnvironmental scienceSedimentOil spillPetroleum engineeringEnvironmental engineeringEnvironmental chemistryChemistryGeologyDispersion (optics)

Abstract

fetched live from OpenAlex

The proposed Energy East pipeline project has raised concerns about potential oil spills in Saint John, New Brunswick. While environmental conditions could pose challenges for using mechanical recovery methods if a spill occurs, chemical dispersant could be an alternative. However, the application of chemical dispersant in shallow water and costal zones remains an issue of debate. Furthermore, the effectiveness of chemical dispersant under different seasonal conditions is yet to be determined. This study attempts to describe a modelling effort to understand the probable distribution of petroleum hydrocarbons in Port Saint John following a hypothetical release of crude oil to which dispersant is applied during different seasons. A three-dimensional model was used to simulate the transport of oil with a release of 1,000 m3 of Arabian light crude in the summer and winter. A stochastic approach took into account the uncertainties of environmental inputs. The results were a significant reduction of oil ashore, and enhanced biodegradation with dispersant application. However, these effects were accompanied by an increase of oil in the sediment and water column, which is a concern. While the results are only conclusive for the selected scenarios of summer and winter, the method could be applied to other months and seasons to support more detailed analysis regarding dispersant application.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.107
GPT teacher head0.339
Teacher spread0.232 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueWater Quality Research JournalSame topicOil Spill Detection and MitigationFrench-language works237,207