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Record W2967512543

Evaluación de bentonita cubana como material absorbente para la recogida de hidrocarburos

2019· article· es· W2967512543 on OpenAlexaboutno aff
Lester Rivas Trasancos, Yamila Navarro Sosa, Maydeibys León Barrios, Miguel Ángel Díaz Díaz, Roberto Romero Silva, Daylén Salazar Alemán, Carlos C. Cañete Pérez

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldEarth and Planetary Sciences
TopicGeological and Tectonic Studies in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsBentoniteAbsorption capacitySorbentGravimetric analysisAbsorption (acoustics)Environmental scienceHydrocarbonWaste managementChemistryAdsorptionMaterials scienceChemical engineeringEngineeringOrganic chemistryComposite material
DOInot available

Abstract

fetched live from OpenAlex

The global environmental regulations for oil spills in soil and water require the application of solutions that mitigate this impact, using absorbent materials. For this reason, there is a tendency to look for materials based on fibers or natural clays, given their high absorption capacity and low costs. The objective of this work is to evaluate the hydrocarbon absorption capacity of two clays: natural acid bentonite and natural sodium bentonite. For the experimental analysis of the maximum absorption capacity of the fuels and crude oil employed, the methodology described in ASTM D 5831-96, F 726-12 and the Canadian protocol Oil Sorbent: Testing Protocol and Certification listing program was applied. Bench scale tests were performed with different concentrations of clays and types of hydrocarbons to determine the absorption capacity of the bentonites evaluated at different concentrations, which were determined by gravimetric method. The best absorption capacity was obtained with the lowest concentration of both bentonites. The statistical analysis of the absorption capacity shows that the fundamental weight is given by the combination of all the factors: concentrations and type of absorber and hydrocarbon.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1350.003

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 head0.255
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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