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Record W2061276906 · doi:10.1080/009083190957649

Qualitative Understanding of the Mechanism of Oil Mineral Interaction as Potential Oil Spill Countermeasure—A Review

2007· article· en· W2061276906 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.

Bibliographic record

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCountermeasureOil spillNatural resource economicsPrestigeCrude oilEnvironmental scienceScale (ratio)BusinessPetroleum engineeringEnvironmental planningRisk analysis (engineering)Environmental protectionEngineeringEconomicsGeography

Abstract

fetched live from OpenAlex

Abstract Industries today are finding it increasingly difficult to cope with the stringent environmental laws. Besides, the resources, both capital and human, are stretched to the limit. The oil industries are seeking enhanced opportunities by merging with one another to cut costs and increase production. Accidental spilling of crude oil often happens in the course of exploration and transportation to consumption. Cleaning the spilled oil is a costly problem on a global scale. The Prestige oil spill in 2002 along the coast of Spain alone was valued at 9 billion dollars, with several occurring per year. Many techniques and methods of oil spill cleanup exist, but it is not clear which one functions better. The aim of this article is to review the emerging, and more economic method, of oil spill remediation—oil-mineral aggregation, (OMA). This article also suggests future areas to better improve its potential as a spill countermeasure.

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)0.000
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 score0.482
Threshold uncertainty score0.514

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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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