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Record W2044768841 · doi:10.4018/jaeis.2012070102

Forecasting High Correlation Transition of Agricultural Landscapes into Urban Areas

2012· article· en· W2044768841 on OpenAlexaff
Federico Martellozzo

Bibliographic record

VenueInternational Journal of Agricultural and Environmental Information Systems · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsMcGill University
Fundersnot available
KeywordsUrban sprawlCellular automatonAgricultureNatural (archaeology)Economic geographyGeographyEnvironmental resource managementPhysical geographyEnvironmental planningLand useComputer scienceEnvironmental scienceCivil engineeringEngineeringArtificial intelligenceArchaeology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.171
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2012
Admission routes1
Has abstractyes

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