Bibliographic record
Abstract
The City of Niagara Falls, Ontario, Canada attracts millions of visitors each year to the Falls and a variety of other attractions the area has to offer. A new People Mover System will be introduced to address this growing need to accommodate visitor trips to the City. A People Mover Parking Strategy Study was conducted to promote system ridership, reduce congestion, and act as a catalyst to economic growth by providing effective parking supplies and policies. The People Mover Parking Strategy will relieve traffic congestion and conflicts associated with required vehicular travel in the tourist areas (i.e. hotel and Casino patrons, local business activities, etc.) and permit further development of prime properties. The People Mover Parking Strategy will accomplish these objectives by (a) the identification of new parking supplies that meet demand profiles, (b) the implementation of a flexible Management Model that addresses stakeholder needs, (c) the involvement of the private-sector in the distribution of People Mover passes and the provision of People Mover parking supply, (d) the implementation of an advanced wayfinding signing strategy, and (e) the amendment of current parking-related by-laws (e.g. zoning, commercial parking, etc.) and policies (e.g. cash-in-lieu and signing policies) to encourage the use of the People Mover lots and to improve traffic circulation around the roadways in the City of Niagara Falls. The paper focuses on those strategies, which supports ridership, the results of the private sector consultation, and highlights the challenges of implementing such a strategic plan in an environment with competing stakeholder requirements.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".