El uso del ferrocemento en la construcción civil. Experiencia cubana
Bibliographic record
Abstract
Resumen: Desde principios de la década del 70 el autor se motivó por la utilización del ferrocemento en la construcción civil en Cuba a partir de conocer su uso por profesionales de Brasil y Canadá. Como resultado de esto se vinculó como proyectista o asesor en la gran mayoría de las obras realizadas con este material en el país. Con una experiencia de más de cuarenta años 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, depósitos, piscinas y otras que hacen de Cuba uno de los países de mayor desarrollo y aplicación de esta tecnología en América. Palabras Clave: ferrocemento, piscinas, embarcaciones, depósitos, 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".