{"id":"W4379054030","doi":"10.3390/s23115248","title":"Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Université Polytechnique Hauts-de-France; Centre National de la Recherche Scientifique; Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi; Polytechnique Montréal","keywords":"Occupancy; Cluster analysis; Transferability; Software deployment; Computer science; Process (computing); Parking lot; Data mining; Dimension (graph theory); Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009478998,0.0001774844,0.00025113,0.0001946798,0.0002263062,0.00004677752,0.0002897063,0.0000726387,0.000008586667],"category_scores_gemma":[0.0001712211,0.0001540329,0.0001207785,0.000488423,0.00004691903,0.0000811819,0.0002404647,0.0001571871,0.00002003175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000151909,"about_ca_system_score_gemma":0.00001615543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001714485,"about_ca_topic_score_gemma":0.0003281804,"domain_scores_codex":[0.9983349,0.00003335354,0.0004111062,0.0002777067,0.0003716093,0.0005713361],"domain_scores_gemma":[0.9989656,0.0004150545,0.000078434,0.0003940887,0.00008495859,0.00006184531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000227194,0.000004213388,0.009033352,0.0003988713,0.00004706893,0.000005654496,0.001344468,0.9839721,0.002339303,0.00002873291,0.0004152955,0.002388192],"study_design_scores_gemma":[0.0002712301,0.00001546877,0.003591076,0.0001924844,0.000008150933,0.000008676676,0.0001933692,0.9890292,0.005898666,0.00008222638,0.0005511617,0.0001583348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814971,0.00007437448,0.01540657,0.00002864432,0.0005404355,0.0005746412,0.00005748211,0.0003400705,0.0014807],"genre_scores_gemma":[0.9981111,0.000005925246,0.0005808053,0.000004746491,0.0001648969,0.00005428908,0.00001893427,0.00007400107,0.0009852473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01661407,"threshold_uncertainty_score":0.6281278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2351732547900467,"score_gpt":0.3151517713477502,"score_spread":0.0799785165577035,"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."}}