Bibliographic record
Abstract
Building Information Modelling (BIM) enables the Civil engineers professional to accomplish the digital requiremnts and also the integration and collaboration on the elaboration of projects and maintenance of buildings. BIM methodology is currently the main subject of investigation and application in the construction industry and the education have been exploring the introduction of this issue in curricular programs. The students of civil engineering and architecture, as future professionals, must updated their skills with the most recent innovative technology and knowledge. Several academies better classified within the architecture and engineer sector, were selected and its curricular programs were analyzed: the didactic strategies of inserting BIM teachings are similar in the main concept and practice, but depending of the expertize of the school, the aspects related to architecture, structures, construction or planning are deeper taught; the level cycles of introduction BIM (bachelor, master or postgraduate), the professional courses offered to architects and engineeres and the main subjects were discussed. The principal aim of the curricular research is the characterization of BIM education in distinct academies. A resume of actions and organization of topics that promotes an adequate updating of the students skills was achived, helping BIM educators in their activity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".