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Record W4220926833 · doi:10.3390/jmse10030425

The Formulation, Development and Application of Oil Dispersants

2022· article· en· W4220926833 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Marine Science and Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsWestern University
FundersFisheries and Oceans Canada
KeywordsDispersantOil spillEnvironmental scienceEnvironmentally friendlyCountermeasureBiochemical engineeringEngineeringEnvironmental engineeringEcologyDispersion (optics)

Abstract

fetched live from OpenAlex

Oil spills in open waters pose a significant threat to marine life. The application of dispersant as an oil-spill response is a promising approach to minimize the environmental burden caused by these accidental events. Dispersants have been accepted and applied by many countries around the world as a countermeasure in responding to oil spills due to their great success and advancements in recent years. This review covers different approaches for design and development of chemical formulas of oil dispersants with the aim to improve dispersing efficiencies, followed by formulating non-chemical dispersants, which are more environmentally friendly approaches. The encouraging properties motivate scientific communities to research and develop these non-chemical-based dispersants. In general, this review intends to offer a multi-perspective overall picture of progress made in recent years to develop and apply different dispersants suitable for combating oil spills.

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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.003
GPT teacher head0.181
Teacher spread0.178 · 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