The next frontier: data-driven urban underground space planning orienting multiple development concepts
Bibliographic record
Abstract
Abstract In recent years, the comprehensive and extensive development of urban underground space (UUS) has gained substantial popularity with the efficient guidance of UUS planning. This study discussed the research trends and paradigm shift in UUS planning over the past few decades. Bibliometric and comparative studies were conducted to identify the contributions of the research in this field. The analysis identified the overall temporal development trend of UUS planning and the research hot spots, namely, the primary use of UUS and UUS planning technology. Additionally, the study identified academic collaborative relationships through country and institution co-occurrence network analysis. The diversified development philosophy, planning systems, key planning scenarios, and data-driven technology pertaining to UUS planning have been extracted through keyword co-occurrence network analysis. Moreover, the planning systems, planning management, and planning practices for UUS in various countries, including Singapore, Japan, Finland, Canada, and China, were also systematically reviewed. By doing so, the worldwide UUS planning evolution has been identified. The paradigm shift for UUS planning has been clarified, involving technical method, result form, control mode, and control elements. Furthermore, the conceptual data-driven framework for UUS planning, which orients multiple development concepts, has been proposed to meet the requirement of next frontier development.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".