Bibliographic record
Abstract
In April 2011, Kelowna City Council approved an Active Transportation Network (ATN) and a fully-funded financing strategy to build over 50km of off-street continuous urban arterials for walking, biking and other forms of active mobility. The ATN will connect all of the major town centres and institutional destinations within the City. The new system is a direct response to several facts. Kelowna is one of the most car-dependent cities in Canada; it has the second highest per-capita carbon footprint attributed to on-road transportation in BC; and it has the oldest demographic in Canada that would see extensive health benefits by engaging safe active mobility options for both commuting and recreational uses. The explicit purpose of the ATN is to support connectivity and stimulate densification of the five existing mixed-use urban centres as envisioned in the 2030 Official Community Plan: Greening our Future (OCP). The ATN is an explicit action item intended to meet the City's GHG emission reduction targets mandated under the BC Climate Action Charter. Additionally, the ATN is a key strategy in future-proofing the city against peak oil and the rising cost of transportation. This program was nominated for the TAC 2011 Sustainable Urban Transportation Award. For the covering abstract of this conference see ITRD record number 201211RT334E.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".