Future public transport options for Toowoomba for the next twenty years \n
Bibliographic record
Abstract
Expanding cities around the world are facing the harsh reality of insufficient public transport systems. As Toowoomba, Australia is a high growth city with a lower than average percent (0.7%) of the population utilising public transport, particularly in the north-eastern suburb of Mt Lofty, it was selected for analysis (Census Data: Toowoomba, 2011). The literature review included the types of public transportation, the need and importance of public transport in cities and the characteristics of an effective public transport system. \n \nMajor factors controlling the current development and use of public transport in Toowoomba are identified. Current public transport networks within Australia and around the world that are deemed effective in their operation are identified. The common technologies and strategies that contribute to an effective public transport network are determined. Within the study area, Mt Lofty, the resident’s opinions are collected to determine a clear picture of what needs to be improved or changed for public transport usage to increase. \n \nThe cities that were selected for a public transport analysis are Wellington, Geelong, Hobart, Hamilton, Barnsley and Saskatoon. Discussion points were collaborated and then recommendations are made. The long and short term recommendations include ‘dial and go service’, longer hours of operations, Sunday services, electronic tickets, routes revision, higher frequency’s services, night services, CDB shuttle and free public transport for students and/or seniors. This study provides recommendations to the Queensland Government that will improve the usage of the public transport service provided by Qconnect in Toowoomba within the next twenty years as the city continues to grow.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
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".