Developing an Evaluation Protocol for the Toronto Public Library (TPL) System Through the Application of a Building Performance Evaluation (BPE) of a Branch Library to Inform its Retrofit Strategy
Bibliographic record
Abstract
Buildings play a significant role in our economy and society. Substantial capital is invested in buildings, and they are the locales where a large portion (e.g., work, cultural, religious, social and personal activities) of our lives are conducted. Despite the significant monetary and temporal investments in buildings, building performance evaluations (BPEs) are not standard practice. From BPEs that have been conducted, important findings have been identified. Significant gaps frequently exist between the design intent of buildings and their measured performance (e.g., energy and water consumption) and user satisfaction (e.g., thermal comfort, lighting, noise). Environmental (e.g., resource consumption) and economic drivers (e.g., productivity, operational costs) are spurring the growth of BPEs. A BPE was conducted of the Weston Public Library (WPL) with the intent of informing a retrofit strategy and developing a protocol for conducting BPEs in the Toronto Public Library (TPL) system.
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 | 0.235 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.019 |
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".