On the Road to Electrification with AMT (Agence Metropolitaine de Transport)
Bibliographic record
Abstract
To reduce transportation-related greenhouse gas (GHG) emissions and continually improve public transportation's competitive advantage over the automobile, we must increase our use of renewable energy, in this instance hydroelectricity, given Quebec's position as the world's fourth largest producer of hydro-power. As stated in its Strategic Plan, one of the AMT's main goals is to increase its reliance on renewable energy in operating the regional public transit system. As automobiles are becoming increasingly energy-efficient, public transit must undertake a swift transition to renewable energy use to keep pace and to enhance its competitive advantage, in addition to making a bigger contribution to sustainability on a region-wide basis. The AMT is already very active in this area and several electrification projects are currently under way, including a major study on the electrification of the commuter train network, the acquisition of hybrid (dual-mode) locomotives, the implementation of a network of electric car plug-in stations and CLIC, the state of the art car-pooling program using electric cars. This paper was originally published in French as L'AMT prend le virage de l'electrification (Agence Metropolitaine de Transport) (see ITRD record rumber 201111RT667F). Thsi project was nominated for the TAC 2011 Sustainable Urban Transportation Award. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.022 |
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".