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Record W2898018336 · doi:10.1080/15320383.2018.1506425

Physical properties of oil-particle aggregate (OPA)-containing sediments

2018· article· en· W2898018336 on OpenAlexaff
Jeffrey A. Johnson, Deborah A. Edwards, Douglas Blue, Sara J. Morey

Bibliographic record

VenueSoil and Sediment Contamination An International Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsDeposition (geology)PorosityParticle (ecology)SedimentAggregate (composite)Particle sizePhysical propertyMineralogyMaterials sciencePorous mediumChemical engineeringGeologyComposite materialGeomorphology

Abstract

fetched live from OpenAlex

Sediments composed of oil-particle aggregates (OPAs) have unique physical characteristics. These in situ deposited sediments develop at locations where a continual or nearly continual discharge of non-aqueous phase liquids (NAPLs) have occurred, or are occurring through time. The NAPL discharged into the surface water body interacts with suspended particles in the water column. The particles adhere to the suspended NAPL, which generally is in the form of a bead, and produce a discrete aggregate. As the aggregate grows in response to additional particle adherence, the density of the unit increases and deposition occurs. The resulting sediment consists of a collection of discrete OPAs that form a network with small pores, where oil is tightly bound and/or contained. Porosity, water content, and dry bulk density measurements indicate the sediment formed by OPA deposition is physically unique. Although the sediment consists of a very open pore structure, the pore openings are relatively small, typically being less than 5 microns in diameter. These small pores inhibit fluid movement. Results of physical property testing suggest the OPA structure is retained upon deposition. Although the sediment contains NAPL, this original OPA structure inhibits the oil beads from coalescing, which would enable NAPL flow.

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 score0.446
Threshold uncertainty score0.468

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 communication0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.236 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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