Bibliographic record
Abstract
Accelerate Kootenays is an innovative regional approach to build a network of electric vehicle charging stations between the Okanagan and Alberta. Increased adoption of electric vehicles (EV) by Kootenay residents will reduce transportation emissions and support local climate action commitments. The goal is to install the base network that will make electric vehicle travel safe, reliable and enjoyable. To make electric vehicle ownership and travel realistic for both rural residents and tourists, a collaboration of industry, government and tourism associations have endorsed and funded this electric vehicle strategy and network development. The Kootenay region of British Columbia has cold winters, rugged terrain and a sparse population. The transportation sector is a large contributor of community greenhouse gas emissions. There is high dependence on personal vehicles with limited alternate forms of transportation given low density and large distances between communities. Accelerate Kootenays is a $1.2 Million project that will transform the connectivity of the region, resulting in accelerated EV adoption and enhanced tourism and economic development. To drive broader EV market adoption, connectivity to rural areas, including parks, tourist destination and small communities must be part of the evolving EV 'ecosystem'. The project employs a unique approach to marketing and community engagement and develops branding opportunities at a regional scale. Explore the expanding scope of the EV ecosystem, and the necessity to look beyond the dense urban centers for robust network development, and consider the opportunities and advantages to connectivity across rural regions in Canada.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".