{"id":"W2118905110","doi":"10.1007/s11067-007-9030-y","title":"Modeling Urban Land Use Change and Urban Sprawl: Calgary, Alberta, Canada","year":2007,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":173,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Calgary","funders":"","keywords":"Urban sprawl; Land use; Geography; Markov chain; Urban planning; Cellular automaton; Computer science; Transport engineering; Cartography; Environmental resource management; Environmental science; Civil engineering; Machine learning; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001438534,0.0001286425,0.0001648677,0.00001179865,0.0001413093,0.0000750508,0.00007195985,0.00008396183,0.00007781849],"category_scores_gemma":[0.00000230301,0.0001133596,0.00001664556,0.00002611551,0.00001224361,0.0002317042,0.0001031053,0.00007788604,0.000003921083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007477429,"about_ca_system_score_gemma":0.00001039312,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9710501,"about_ca_topic_score_gemma":0.9981487,"domain_scores_codex":[0.9992079,0.000009541423,0.0001953791,0.0002505408,0.00004175758,0.0002948412],"domain_scores_gemma":[0.999589,0.00005363519,0.0000485343,0.0001212271,0.00000282496,0.0001847999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002749846,0.000007505802,0.9554592,0.000007074721,0.00001182639,0.000004880859,0.0001856985,0.03583441,3.517262e-7,0.00002533984,0.0002489849,0.008187254],"study_design_scores_gemma":[0.0002185502,0.00002110416,0.0795139,0.00000888036,0.00001191452,0.000005074879,0.00001426356,0.9070503,0.00000157842,0.00001197758,0.01297862,0.0001638185],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997456,0.0001956163,0.0009978734,0.00009189265,0.000241501,0.0001292454,0.000005133582,0.000007793562,0.0008749064],"genre_scores_gemma":[0.9983991,0.0004148469,0.0000420548,0.000667866,0.0003920773,0.000004179632,0.00001605318,0.00001364406,0.00005020792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8759453,"threshold_uncertainty_score":0.4622671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0127508191727459,"score_gpt":0.172894175346093,"score_spread":0.1601433561733471,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}