Bibliographic record
Abstract
Abstract Urban land-use planning has traditionally assumed that core functions—industry, housing, office, and retail—require expansive, permanent physical footprints. This physicality paradigm, inherited from the beginning of organized urbanism, is now challenged by rapid digitization and intensifying environmental and climate pressures. Increasingly, tasks once anchored to factories, offices, and storefronts migrate to automated, remote, or virtual platforms, undermining the notion that physical expansion must track economic or social progress. Confronted with global urbanization and looming climate emergencies, digitization compels a reevaluation of how cities allocate land, consume resources, and protect vulnerable communities. This paper introduces a dynamic “meltdown” framework for understanding how digitization systematically erodes structural reliance, thereby freeing or repurposing land for adaptive reuse. Drawing on spatio‐temporal big data from sensor networks, remote sensing, geographic information systems, and occupant analytics, we examine how key urban tasks—production, commerce, administration, and residency—can be quantified for “meltability” based on physical anchorage, digital capacity, and environmental constraints. Our model demonstrates that meltdown not only diminishes structural demand but also opens opportunities for greener infrastructure, such as flood buffers or urban forests, thus enhancing climate resilience. By integrating real‐time data and occupant‐centered metrics, planners and policymakers can anticipate where and when digital alternatives render conventional land uses obsolete, proactively converting those areas to more sustainable or socially beneficial functions. In doing so, this research transcends conventional “smart city” optimization, revealing how occupant activities disrupt once‐immutable footprints and forging a data‐driven path to reduce carbon emissions, strengthen ecosystem services, and help equitable, knowledge‐driven urban development under mounting climate challenges.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".