{"id":"W1582061741","doi":"10.1109/lisat.2015.7160219","title":"IPLMS: An intelligent parking lot management system","year":2015,"lang":"en","type":"article","venue":"","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Parking guidance and information; Parking lot; Parking space; Flexibility (engineering); Management system; Computer science; Fuzzy logic; Transport engineering; Real-time computing; Engineering; Artificial intelligence; Operations management","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006648367,0.0001406857,0.0001563405,0.000166788,0.0000335439,0.0001008189,0.0002832191,0.00005145829,0.00003423956],"category_scores_gemma":[0.00000707262,0.0001288115,0.00003247887,0.0002084969,0.00001388969,0.0001268888,0.00007785849,0.0001045731,0.0009543202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004009028,"about_ca_system_score_gemma":0.0000103627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000840074,"about_ca_topic_score_gemma":0.00003461886,"domain_scores_codex":[0.9986892,0.00005648018,0.0002528034,0.0002003955,0.0004370289,0.0003640963],"domain_scores_gemma":[0.9992081,0.00001898337,0.00001382802,0.0004527367,0.00005259904,0.0002537547],"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":[0.000121235,0.0003563561,0.02885131,0.00785816,0.001725054,0.00140549,0.01038078,0.4222138,0.002095073,0.1409345,0.1801114,0.2039468],"study_design_scores_gemma":[0.0006196899,0.0001177778,0.001004578,0.0003746544,0.00002473681,0.00008011426,0.01293585,0.5743023,0.004615508,0.00007820412,0.4052139,0.000632711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1889727,0.0006559077,0.1370665,0.00002905524,0.002927617,0.0009565622,0.000002665428,0.003704554,0.6656844],"genre_scores_gemma":[0.9953616,0.000008123874,0.00256673,0.00000889794,0.0001708383,0.00006654521,0.000004434224,0.00004871341,0.001764076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.806389,"threshold_uncertainty_score":0.9998236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06416123809039434,"score_gpt":0.2826454706461036,"score_spread":0.2184842325557093,"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."}}