Digitizing the physical prey: A Lotka–Volterra model for knowledge-driven meltdowns in urban building usage
Bibliographic record
Abstract
This paper proposes and validates a Lotka–Volterra (LV) meltdown model to explore how digital predation—fueled by occupant knowledge—systematically reduces or “consumes” industrial, office, retail, and housing footprints. In contrast to static land-use frameworks, intangible usage acts as a “predator” expanding at the expense of physical “prey” whenever occupant skills surpass key thresholds. By embedding meltdown fractions with structural anchoring, digital readiness, and vacancy, we capture the concurrent erosion of physical functions and the rise of intangible domains. We calibrate the model using 125 years (1900–2025) of data spanning four major usage categories, showing how occupant knowledge explains historical shifts—such as the collapse of industrial footprints or partial contractions in retail—once tasks migrate online. Forward scenarios to 2100 suggest near-elimination of Industrial usage, severe Retail retrenchment, modest Office reductions, and a stable Housing share. Crucially, partial reallocation accommodates historical building conversions (e.g., repurposed factories) and intangible migrations, demonstrating that digital saturation can drive meltdown flows fully online after mid-century. Although intangible usage never forms a universal “monolith”—thanks to intrinsic model decay—knowledge-driven digitization may drastically accelerate meltdown unless on-site distinctiveness or policy interventions sustain physical categories. By casting occupant knowledge in a Lotka–Volterra predator–prey framework, this study offers a novel lens to interpret and project the fate of physical building stocks amid intensifying digital transformation. • Adapt predator–prey logic to occupant knowledge dissolving building footprints. • Digital readiness triggers meltdown in Industrial, Retail, and Office footprints. • 125-year data reveals occupant tasks vanish once digital thresholds are exceeded. • Gravity-based partial reallocation generates reuse or intangible expansions. • Future scenarios project near-elimination of categories by 2100 if meltdown persists.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".