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Record W1991461251 · doi:10.7901/2169-3358-2005-1-1069

THE DYNAMICS OF ORIMULSION IN WATER WITH VARYING ENERGY, SALINITY AND TEMPERATURE

2005· article· en· W1991461251 on OpenAlexaff
Merv Fingas, Ben Fieldhouse, Zhendi Wang, Mike Landriault

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2005
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBrackish waterSalinityBuoyancyTRACERChemistryEnvironmental scienceThermodynamicsGeology

Abstract

fetched live from OpenAlex

ABSTRACT Orimulsion is an oil-in-water emulsion of Venezuelan bitumen. Orimulsion was found to behave predictably in salt (33°/oo NaCl) and fresh water, driven by buoyancy to rise in salt water and sink in fresh water; but behaviour in brackish water (20°/oo NaCl) was difficult to predict. Temperature has also been indicated as having an influence on Orimulsion behaviour. The current study extended experimentation down to lower temperatures, and a variety of mixing energies. This study resulted in new information on the behaviour of Orimulsion spills in salt, fresh, and brackish water with salinity values of 20 and 33 degrees at temperatures of 5 and 15 degrees Celsius. Energy was varied by adjusting the applied rotational field and measurement with thermal probes. Depletion rates and characteristics were determined by adding Orimulsion to a 300-L tank of water, taking a time series of samples, and determining the concentration of bitumen and the particle size distribution. Changes in bitumen concentration and particle size distribution as a function of time were also measured. Using these data, simple equations were developed to describe and predict the concentration of bitumen in the water column as a function of time. Similarly nomograms showing the amount of oil on the bottom and on the water surface are presented.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.276

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 head0.007
GPT teacher head0.226
Teacher spread0.218 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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