Buildings 360º: un nuevo enfoque para la enseñanza en construcción
Bibliographic record
Abstract
El presente trabajo tiene como objeto presentar el proyecto de innovación educativa Buildings 360º. Proyecto que toma el relevo de iniciativas educativas anteriores, pretendiendo desarrollar una novedosa plataforma inmersiva que permitirá consultar las obras de construcción a lo largo del tiempo. Plataforma que abogará por el uso de imágenes 360º en contraposición de enfoques más extendidos basados en costosos modelos 3D, dando lugar a un sistema de bajo coste, muy intuitivo, capaz de gestionar grandes bloques de información y fácil de implementar por usuarios no expertos. Este sistema se usará como complemento docente en la asignatura de Construcción I y Construcción II, permitiendo al alumnado consultar cada una de las fases de una obra, sus materiales y sistemas constructivos. We present here the innovative education project Buildings 360⁰. This project follows previous educative initiatives. The project goal is to develop a novel immersive platform that will enable students to view real construction works along time. This platform will use 360 images in contrast to the widespread and expensive 3D models resulting into a low-cost system which is at the same time very intuitive, allows to manage large amounts of data and is easy to implement by non-expert users. This system will be used as a complementary material in the subjects of Construction I and II and will enable students to look up each of the phases of the construction work, the materials and the construction systems.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
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".