Forecasting High Correlation Transition of Agricultural Landscapes into Urban Areas
Bibliographic record
Abstract
One of the most critical challenges modern society is facing deals with the uncontrolled spread of urban environment among surrounding natural environments, with agricultural and wild landscapes being the ones that suffer the effects of urban and suburban pressure (Ewing, 1994). This phenomenon known as urban sprawl is controversial because even if its conceptually well known there is not a universally shared definition of causes and consequences (Brueckner, 2002). The main aim of this research is to develop a methodology to successfully investigate the evolution trend of sprawl on a specific case study and to get empirical evidences of the tight correlation between urban growth and loss of agricultural and natural lands over time. The research is developed in two phases: the diachronic analysis of landcover changes occurred in the recent past through the use of satellite imagery; the forecast of possible landcover scenarios through the use of cellular automata model. Combining the results obtained in the two phases will build up a longer time span on which investigate the phenomena previously described.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".