Bibliographic record
Abstract
Following a decade of heavy investment in busways, most notably in cities such as Brisbane, light rail has re-emerged as an inner-city transit investment for many Australian cities. In the next decade Australian light rail network size is expected to grow by about a quarter with new routes on the Gold, Coast, Sydney, Canberra and Perth. Analysis shows that Australian light rail is dominated by the substantial Melbourne streetcar network, which is one of the largest in the world. Although light rail has not expanded much in terms of network size over the last decade, ridership growth has been substantial (+46% between 2001-2 and 2011-12) and well above system-wide (all mode) public transport ridership growth in most cities. In general, service levels on Australian trams are low compared to European and North American systems. Also service levels have not kept pace with ridership growth, acting to increase the ridership productivity of most Australian light rail over the last decade. Melbourne leads Australia in terms of ridership productivity (passengers per vehicle kms) and Melbourne tram route 109 has the highest light rail route ridership in Australia (935K p.a.) and the highest ridership effectiveness (11.5 boardings per vehicle km). While the contemporary history of light rail planning has focussed on what might be termed the 'streetcar struggle', medium term plans for new system development see light rail as a solution for urban access, urban redevelopment and the provision of reliable and higher capacity in congested inner urban contexts.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".