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Record W2980704609 · doi:10.30799/jespr.176.19050305

Influence of Climatic Parameters on Changes in the Density and Viscosity of Diluted Bitumen after a Spill

2019· article· en· W2980704609 on OpenAlexaffabout
Thomas King, Patrick Toole, Brian Robinson, Scott Ryan, Kenneth Lee, Michel C. Boufadel, Haoshuai Li, Jason A. C. Clyburne

Bibliographic record

VenueJournal of Environmental Science and Pollution Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsSaint Mary's UniversityBedford Institute of Oceanography
Fundersnot available
KeywordsAsphaltEnvironmental scienceViscosityAttenuationOil spillWind speedAtmospheric sciencesGeologyEnvironmental engineeringOceanographyMaterials science

Abstract

fetched live from OpenAlex

Nonconventional oil products (Access Western Blend [dilbit], Western Canadian Select [dilsynbit], and Synthetic Bitumen [synbit]) and a conventional crude (Heidrun) were naturally weathered on sea water under spring (April-May) and summer (July-August) conditions to improve our understanding of the effects of climatic parameters (air and water temperature, wind speed, and light intensity [solar radiation]) on their density and viscosity. The physical properties data, from each experiment, was fitted to a previously developed hyperbolic function that captured different rates of changes in densities and viscosities of the oils due to preferential weathering of the diluent portion of the diluted bitumen products. A combination of multiple correlation and regression analysis of experimental data over two seasons revealed that there were significant (p<0.05) trends among the measured climatic parameters and the changes in the densities and viscosities of oils after a spill. Analysis of variance (repeated measures) showed that seasonal conditions had a significant (p<0.01) effect on the changes in the physical properties of all oils after release. These results on key factors which control the rates of natural attenuation for nonconventional oils spilled on water will aid decision making during oil spill preparedness and response operations.<div><br></div>

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.472

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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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