Room-level data of Simulated Energy consumption and Ventilation dynamics (RSimEV)
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
This dataset offers simulated data that includes various parameters impacting energy consumption and ventilation across diverse building scenarios. The simulations encompass various room types within buildings of varying shapes and sizes. Comprising a total of 312 CSV files, each file corresponds to simulations conducted in different rooms within buildings with random parameters. Each building undergoes 200 simulations for a one-month period, with the month randomly chosen to account for different weather conditions. Locations are randomly selected from three regions in the north hemisphere: 1) Dusseldorf, North Rhine-Westphalia, Germany; 2) Tehran, Tehran, Iran; and 3) Brockville, Ontario, Canada, representing three climate zones (mixed, warm, and cold). The simulations yield hourly results, resulting in file sizes ranging from 144,000 (representing 200 simulations over 24 hours for 30 days) to 148,800 data rows (for simulations spanning 31 days). Each CSV file is structured with 55 columns, capturing a comprehensive set of attributes relevant to energy consumption and ventilation dynamics. The collective dataset includes 45,562,639 rows, presenting a robust foundation for in-depth analysis and exploration of the intricacies of building performance across many conditions and configurations. It's essential to note that users are accountable for any risks associated with the dataset's utilization, and the creators explicitly disclaim responsibility for specific applications or outcomes. Detailed information on dataset columns and their units is available in the accompanying "readme.txt" file.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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