Bibliographic record
Abstract
This is a comprehensive review of sustainability practices and policies as applied to transit, given the increasing awareness that there are multiple components to the sustainability “umbrella,” including land use, water use, soil use, and solid waste disposal, in addition to carbon emissions. Transit agencies are also extending green practices to construction and even location of their facilities, such as spacing bus depots so that travel time is reduced, thereby reducing fuel consumption. The American Public Transportation Association (APTA) has launched an internal Sustainability Commitment, developed by an in-house task force to help members learn about best practices developed by their industry peers. Elsewhere, New York MTA’s Blue Ribbon Commission on Sustainability released 100 recommendations for strategies and technologies to reduce the region’s carbon footprint. One calls for the MTA to draw 80 percent of its energy from clean, renewable sources by 2050, since it is a very large user of power in the region. Ninety percent of its power is for traction for commuter rail and subways. Building bus rapid transit (BRT) is another green approach. The Toronto Transit Commission, which provides more than 450 million rides a year, is on track to meet a goal of buying 25 percent of its power from renewable sources by 2012. It is also tightening its recycling efforts throughout the supply chain. Other transit agencies whose green practices are described include the Massachusetts Bay Transportation authority, the Champaign Urbana Mass Transit District, Toronto’s GO Transit, and the firm that operates the Altamont Commuter Express rail service in California.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".