Bibliographic record
Abstract
The article describes the web-based Building Intelligence Quotient (BiQ) programme, developed for the Continental Automated Buildings Association (CABA). The initial BiQ beta testing and the BiQ corporate portfolio analysis are reported. The BiQ rates building automation systems in existing large office buildings and supports the integrated design and life-cycle costing processes. It contributes to the increasing convergence of green and intelligent building programmes. Using the BiQ for rating the intelligence of building automation contributes to the knowledge of building performance and enhances intelligence within the building industry. The BiQ reflects the convergence of information technology to all building and business enterprise systems contributing to the acquisition of knowledge that can positively guide future decision making and action. Using the BiQ and comparing the BiQs across corporate portfolios provides new ways that various stakeholders can learn from actual building performance and improve decision making for the automation systems of future buildings. The BiQ can be a key part of developing individual and collective intelligence in relation to building design and operation. The BiQ report is a feedback mechanism that shows what is working and what is not. The BiQ and other CABA programmes contribute to the notion of feedback and intelligence.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".