Proceedings of the 8th International congress on architectural technology (ICAT 2019): architectural technology, facing the renovation and refurbishment challenge.
Bibliographic record
Abstract
During the past decade, the construction industry focus has largely been on the many new challenges brought about by the sustainability/climate change agenda and the introduction of BIM in new build. The theme of the next ICAT conference will focus on all issues related to the renovation, refurbishment and re-use of existing buildings. Given the fact that more than half of the industry’s activity is dedicated to these types of building work, the focus seems highly needed. Architectural technology is at the core of the industry where the interplay of many factors creates varied interfaces. Academics and professionals associated with the discipline are ideally placed to lead as we strive to enhance the design, delivery and performance of existing and new buildings as well as the industry at large. This congress will be a vehicle to disseminate research, education and practice at these interfaces.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.084 | 0.034 |
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".