THE GPI TRANSPORTATION ACCOUNTS: SUSTAINABLE TRANSPORTATION IN HALIFAX REGIONAL MUNICIPALITY Prepared by:
Bibliographic record
Abstract
In June 2006, Halifax Regional Municipality (HRM) approved its first Municipal Planning Strategy (MPS), as an amalgamated municipality. The Municipal Planning Strategy sets the general framework for planning decisions over the next 20 years. The major objectives of this initiative are to manage a moderate level of population growth, minimize the environmental impact of that growth, and use it as a catalyst to make HRM more sustainable in all of its activities. Due to the dispersed nature of HRM and the diverse mix of its urban, suburban, and rural areas, the links between communities become a key focal point for sustainability measures. Consequently, transportation issues have become central to many of HRM’s current planning decisions and are a key component of the MPS. The GPI Transportation Accounts: Sustainable Transportation in Halifax Regional Municipality are intended to aid HRM’s transportation planning process. The transportation indicators and full cost accounting of passenger transportation in HRM outlined in this report can provide HRM planners with a useful model both for assessing the current transportation system and for monitoring its progress towards greater sustainability as the MPS is implemented. Please see GPIAtlantic’s Transportation Accounts: Sustainable Transportation in Nova Scotia for an indepth
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 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".