Bibliographic record
Abstract
Due to Melbourne's ongoing growth, there is continuous pressure on its transportation infrastructure. Further, to maintain its position as one of the most livable cities in the world, Melbourne needs to always look at ways to optimize technology and lifestyle while being conscious of its effects on the environment in order to encourage a sustainable development agenda. Such a stance is part of Melbourne's future sustainable urban development strategy including ‘Melbourne 2017-2050.' As a part of such strategy, this article discusses the possibility of underground urban structures (UUS) to further alleviate Melbourne's continuous urban development problems. Four case studies, Lujiazui, Hongqiao, Montreal, and Helsinki, were studied. These four case studies have some comparability with Melbourne's CBD. Particularly, both Montreal and Helsinki have relevance to Melbourne which is appealing. Predominantly, these two cities' main objective of UUS matches that of Melbourne's long-term urban planning goals. Noticeably, improving the livability along with reducing building operational costs are central to Melbourne's 2017-2050 planning and beyond. According to Melbourne 2017-2050, as a sustainable urban development focus, the city's high livability needs to be maintained together with finding alternative ways to reducing building operational costs. This research would thus serve as a springboard to further investigate the UUS for Melbourne city.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".