Bibliographic record
Abstract
Global Positioning System receivers are used to monitor the parking behaviour of 76 Winnipeg-area residents to determine the most suitable parking lots in Winnipeg for plug-in hybrid electric vehicle (PHEV) recharge infrastructure. Optimizing the location of this infrastructure will help maximize the environmental benefits of PHEVs while minimizing the economic costs. Using a Geographic Information System (GIS) parking events were superimposed on a high-resolution aerial photograph of the city to identify the most potentially suitable parking lots in the city. A parking lot suitability index was then developed to quantitatively rank parking lots in the city based on the parking events associated with them. Variables derived from these groups of parking events include: 1) the number of unique participants that used the parking lot, 2) the median parking duration, and 3) the ratio of parking events that occur during off-peak vs. on-peak electric demand times. The most suitable parking lot as determined by this index was surprising as it would not be considered a ‘major parking lot ’ by the majority of Winnipeggers, suggesting that this kind of study reveals information about parking lots that would not otherwise be apparent. Thus, future studies are recommended. Such studies would benefit from adapting their methodologies based on the strengths and weaknesses of this study, a discussion of which are provided in the last section of the report. ii
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 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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 teacher head, 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".