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Record W4402725684 · doi:10.32604/jrm.2024.056004

Why Sustainable Porous Carbon Should be Further Explored as Radar-Absorbing Material? A Comparative Study with Different Nanostructured Carbons

2024· article· en· W4402725684 on OpenAlexaff
Alan Fernando Ney Boss, Manuella Gobbo de Castro Munhoz, Gisèle Amaral-Labat, Rodrigo Gabas Amaro de Lima, Leonardo Iusuti de Medeiros, Nila Cecília Faria Lopes Medeiros, Beatriz Carvalho da Silva Fonseca, Flavia Lega Braghiroli, Guilherme Frederico Bernardo Lenz e Silva

Bibliographic record

VenueJOURNAL OF RENEWABLE MATERIALS · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersUniversidade Estadual de Santa CruzConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPorosityMaterials scienceCarbon fibersRadarPorous mediumEnvironmental scienceEngineeringAerospace engineeringComposite materialComposite number

Abstract

fetched live from OpenAlex

Radar Absorbing Materials (RAM) are a class of composites that can attenuate incident electromagnetic waves to avoid radar detection. Most carbon allotropes that have the potential to be used as RAM are either carbon nanotube... | Find, read and cite all the research you need on Tech Science Press

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

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