Tendencias de investigación y desarrollo en el área de diseño y construcción de edificaciones
Bibliographic record
Abstract
En este trabajo se presenta un resumen de los resultados alcanzados con la investigacion “Sistema de Deteccion y Monitoreo sobre Tecnologias de Diseno y Construccion en Edificaciones. Diagnostico de las Tendencias de Investigacion y Desarrollo en los ultimos 10 anos”, que tuvo por objeto detectar las tendencias tecnologicas en la investigacion, produccion y oferta de materiales, componentes y tecnologias para la construccion de edificaciones a traves del analisis de los principales centros de I y D de los paises que marcan la pauta de desarrollo de nuevas tecnologias: Estados Unidos, Canada, Francia, Australia, Singapur y Espana. Asi como los de aquellos latinoamericanos con mayor desarrollo e impacto en la economia regional como lo son: Brasil, Argentina, Mexico, y Venezuela. Este sistema ayudara a mantener actualizada la red de investigadores que se desempenan en el pais en el area de estudio. Descriptores: Inteligencia Tecnologica Competitiva; Informacion sobre actitudes de IyD en tecnologia de construccion de edificaciones; Revistas en tecnologia de construccion de edificaciones. Abstract In this essay we present a summary of the results achieved through the research titled: “Identification and Monitoring System of Building Design and Construction Technologies. Diagnosis of Research and Development Trends in the last 10 years”, the purpose of which was to identify the technological trends in the research; production; and offer of materials, components, and technologies for the construction of buildings through the analysis of the main RD as well as Latin American countries which have the greatest development and impact on regional economies, such as: Brasil, Argentina, Mexico, and Venezuela. This system will aid in maintaining the country´s researchers network up to date in this field. Descriptors: Competitive Technological Intelligence, Information on RD Magazines on building construction technologies.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".