Descriptor: Simon Fraser University Electric Vehicle Parking Dataset (SFU-EVP)
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
Simon Fraser University (SFU) aims to make a significant contribution to the study of electric vehicle (EV) utilization and power grid management by providing a comprehensive dataset [Simon Fraser University electric vehicle parking dataset (SFU-EVP)] of EV charging sessions since 2019. This dataset will be continually updated in the future. This extensive dataset presents valuable information on EV charging patterns, providing critical input for power grid planning, policy development, rate design, and infrastructure placement. It also offers opportunities to improve load forecasting, ensure grid stability, and improve the integration of renewable energy. Furthermore, data can facilitate research toward optimizing various vehicle-to-grid (V2G) services, including harnessing EVs as distributed energy storage systems. All data are stored in the commonly used and easily accessible comma-separated value (CSV) file format. By making this dataset publicly available, SFU has created a vital dataset that can drive further innovation and efficiency in EV technology and grid management, fostering a more sustainable and environmentally friendly future. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>IEEE SOCIETY/COUNCIL</b> Power and Energy Society (PES) <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>DATA TYPE/LOCATION</b> Time-Series; SFU Campuses, Metro Vancouver, Canada <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>DATA DOI/PID</b> 10.21227/ya1w-m583
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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