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Record W2134213295

El uso del ferrocemento en la construcción civil. Experiencia cubana

2014· article· es· W2134213295 on OpenAlexaboutno aff
Hugo Wainshtok Rivas, Yenliu Lizazo Hernández

Bibliographic record

VenueRevista científica de Arquitectura y Urbanismo · 2014
Typearticle
Languagees
FieldEnergy
TopicEnvironmental and Ecological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtCartographyGeography
DOInot available

Abstract

fetched live from OpenAlex

Normal 0 21 false false false MicrosoftInternetExplorer4 Resumen : Desde principios de la decada del 70 el autor se motivo por la utilizacion del ferrocemento en la construccion civil en Cuba a partir de conocer su uso por profesionales de Brasil y Canada. Como resultado de esto se vinculo como proyectista o asesor en la gran mayoria de las obras realizadas con este material en el pais. Con una experiencia de mas de cuarenta anos y numerosas publicaciones sobre el tema, el autor analiza en este trabajo las aplicaciones del ferrocemento y sus resultados en construcciones tan diferentes como embarcaciones, mobiliario urbano, viviendas, depositos, piscinas y otras que hacen de Cuba uno de los paises de mayor desarrollo y aplicacion de esta tecnologia en America. Palabras Clave : ferrocemento, piscinas, embarcaciones, depositos, viviendas. Abstract : Since the beginning of 1970s,the author has been involved in the use of Ferrocement for the civil construction in Cuba. He learned much by studying the work of engineers from Brazil and Canada. He has also worked as a planner or an adviser in the field of building construction throughout Cuba. With more than 40 years of experience, and several monographs and books published, the author describes in this paper the application and its results in different types of buildings like vessels, urban works, monuments, houses, tanks, swimming pools and others, which placed Cuba as one of the countries with larger use and development of ferrocement. Keywords : ferrocement, swimming pools, vessels, tanks, houses. /* Style Definitions */ table.MsoNormalTable {mso-style-name:Tabla normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;}

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.865

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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 designNot applicable
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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

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