Advanced Cluster and Predictive Analysis Tool Development for Commercial Office Real Estate Energy Usage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
From 2009-2015, REALPAC collected monthly energy usage and building characteristics for over 500 buildings in the 20 by ‘15 Energy Benchmarking Survey (REALPAC, 2009). While preliminary analysis had been completed on this dataset, this research undertook an in-depth statistical analysis of the data to identify trends and important variables. Eight machine learning algorithms were employed to predict energy usage as a function of previous energy use and select physical features. The dataset did not possess the appropriate variables to predict such usage accurately. Characteristics such as building system efficiency, construction assemblies, condition, compactness, and window to wall ratio are thus recommended for inclusion in future data-gathering initiatives. https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW_RSCR-4.80MB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-ExTa-428KB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-ExGa-5.62MB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-DATA-1.9MB/view
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it