The next frontier: data-driven urban underground space planning orienting multiple development concepts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it