{"id":"W3164984843","doi":"10.3390/ijgi10060358","title":"Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Ontology; Land use; Computer science; Semantics (computer science); Land-use planning; Deep learning; Artificial intelligence; Urban planning; Artificial neural network; Data science; Machine learning; Transport engineering; Engineering; Civil 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":[0.0001406027,0.00009377879,0.0001259194,0.0001767802,0.00002919106,0.00009449486,0.00008395337,0.0000456204,0.000007354847],"category_scores_gemma":[0.00003514121,0.00009332728,0.00002706869,0.00009927576,0.000009752812,0.002205471,0.00003073851,0.0002126555,3.221531e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002344426,"about_ca_system_score_gemma":0.00004061236,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01608404,"about_ca_topic_score_gemma":0.1725218,"domain_scores_codex":[0.9990637,0.00003584346,0.0004829469,0.00005054535,0.0002590154,0.0001079447],"domain_scores_gemma":[0.9995439,0.00003150573,0.0001239517,0.00005882106,0.0001986874,0.0000431064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002120862,0.0002675476,0.06436548,0.00008376968,0.0005206465,0.005471746,0.01047425,0.2636677,0.000003747275,0.0002742612,0.02650513,0.6281536],"study_design_scores_gemma":[0.001706564,0.000146394,0.05726804,0.00007263583,0.00003846234,0.004796852,0.002282221,0.9174972,0.000007117448,0.000005677628,0.01604463,0.0001342186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718039,0.0001816829,0.02423243,0.0003203238,0.002971101,0.0002227905,0.00002660528,0.0001328797,0.000108264],"genre_scores_gemma":[0.9989413,0.000403347,0.0002191914,0.0002491004,0.000127214,0.00001316273,0.00003997344,0.000004565865,0.000002120109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6538295,"threshold_uncertainty_score":0.990468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752058698034551,"score_gpt":0.2438088853032442,"score_spread":0.2262882983228987,"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."}}