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Fate and behaviour of weathered oil drifting into sea ice, using a novel wave and current flume

2020· article· en· W3047143847 on OpenAlexaff
Ivar Singsaas, Frode Leirvik, Per S. Daling, Chantal C Guénette, Kristin Rist Sørheim

Bibliographic record

VenueMarine Pollution Bulletin · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
FundersNorges Forskningsråd
KeywordsSea iceDrift iceGeologyArctic ice packFlumeOceanographyCurrent (fluid)ArcticSubmarine pipelineLead (geology)Environmental scienceGeomorphologyDisplacement (psychology)

Abstract

fetched live from OpenAlex

Increased knowledge about the fate and behaviour of weathered oil in different sea ice conditions is essential for our ability to model oil spill trajectories in ice more precisely and for oil spill response decision making in northern and Arctic areas. As part of the 3-year project: "Fate, Behaviour and Response to Oil Drifting into Scattered Ice and Ice Edge in the Marginal Ice Zone", a novel wave and current flume was built to simulate these processes in the laboratory. This paper discusses some of the findings from this project, which included Marine Gas Oil and four Norwegian crude oils. All crude oils were weathered prior to testing, simulating having drifted on the sea surface for a period (tentatively 1-3 days) before encountering ice. The build-up of oil drifting against an ice barrier and horizontal and vertical migration of oil droplets under solid ice and in frazil ice was studied.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

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 head0.019
GPT teacher head0.223
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2020
Admission routes1
Has abstractyes

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