{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000550956,0.0004407558,0.0002868251,0.000851618,0.002075195,0.001533005,0.001699041,0.0008619369,0.001354117],"category_scores_gemma":[0.001908636,0.0001793042,0.0004425706,0.003582904,0.001074993,0.0007303896,0.0007808934,0.0008724274,0.0002018419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02619764,"about_ca_system_score_gemma":0.0160129,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9890988,"about_ca_topic_score_gemma":0.993286,"domain_scores_codex":[0.9995389,0.00006557723,0.00002591779,0.00009792463,0.0001336693,0.0001380065],"domain_scores_gemma":[0.9992224,0.000235633,0.00005319573,0.0000598716,0.0003300172,0.00009878465],"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":[0.0003867132,0.001076897,0.4316878,0.0003652768,0.0002372594,0.01573641,0.008186628,0.3153674,0.00352393,0.01734992,0.03238266,0.1736991],"study_design_scores_gemma":[0.00007270181,0.0001031439,0.2133735,0.0001251497,0.0001191759,0.0004221088,0.02762647,0.7171865,0.002642971,0.003469845,0.03474751,0.0001109381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827932,0.0003089384,0.003529056,0.001781851,0.00003215772,0.0001217023,0.002880588,0.0001833327,0.00836925],"genre_scores_gemma":[0.9846565,0.000437098,0.006199336,0.00019208,0.000008563605,0.00003876407,0.00327833,0.00004508291,0.005144299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02619764,"threshold_uncertainty_score":0.1900781,"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."}}