Measuring Urban Sprawl, Coalescence, and Dispersal: A Case Study of Pordenone, Italy
Bibliographic record
Abstract
A critical challenge of global change is managing the uncontrolled spread of cities into their surrounding rural and other land. The phenomenon of urban ‘sprawl’ is well known, but it remains controversial because there are no universal definitions about its etiology, nor of the causes and variables related to it. The goal of this study is to depict the temporal trend of sprawl, so as to identify a ‘sprawl signature’ and its evolution for the Italian Province of Pordenone focusing exclusively on spatial dispersion features. Data were compiled from multitemporal remote sensing and used to delimit urban expansion over time. We aim to describe the spatiotemporal patterns associated with urban sprawl using the perspective of the cyclical urban growth theory and focusing on measures that can detect the degree of spatial dispersion during time related to sprawl both in past and projected urban forms. Exactly how the spatiotemporal patterns of urban growth are identified is crucial for urban planners, as knowledge of them allows more efficient calibration of policies to control land-use change in order to satisfy specific needs of the population and prevent the risks and costs related to sprawl.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".