{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004215028,0.0005156654,0.0006064019,0.0007481096,0.0003160459,0.0006264175,0.00131785,0.0004761933,0.000948338],"category_scores_gemma":[0.001466123,0.0004002021,0.000781889,0.00100257,0.0002457764,0.001228685,0.0008122493,0.0005787434,0.0003677103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007100922,"about_ca_system_score_gemma":0.0008624364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02089031,"about_ca_topic_score_gemma":0.02214983,"domain_scores_codex":[0.9997944,0.00004352625,0.00001378667,0.00007213826,0.0000376767,0.00003843439],"domain_scores_gemma":[0.9996142,0.00009755374,0.00005689196,0.00009549233,0.000101926,0.00003401694],"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":[0.00004594009,0.00003578531,0.003687959,0.00002087753,0.00003760823,0.000037845,0.00004686624,0.9732561,0.001334553,0.001863272,0.0007455006,0.01888775],"study_design_scores_gemma":[8.797201e-7,0.000003831602,0.0003800971,0.000001201511,0.00000286832,0.000005820862,0.00001162715,0.9985007,0.0002612921,0.0006672092,0.0001616802,0.000002847291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2864432,0.0002209455,0.7080335,0.0002661336,0.00005851504,0.0000737257,0.000932695,0.001411172,0.002560232],"genre_scores_gemma":[0.9511766,0.00008827576,0.04699416,0.00002798999,0.00001551891,0.00004877901,0.0009382497,0.00005109657,0.0006593264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02089031,"threshold_uncertainty_score":0.0415374,"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."}}