{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003303136,0.0004038421,0.0005228439,0.0007557134,0.0003863324,0.0009079264,0.001761487,0.0005429591,0.007957225],"category_scores_gemma":[0.0004761191,0.0002569011,0.0003203252,0.0003792231,0.0002925308,0.00124144,0.001232696,0.0005279855,0.003203829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005101796,"about_ca_system_score_gemma":0.0006451529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001806058,"about_ca_topic_score_gemma":0.001502147,"domain_scores_codex":[0.9996644,0.00003963555,0.00002890039,0.00008624254,0.0001365164,0.00004421192],"domain_scores_gemma":[0.9997504,0.0000248648,0.00002545831,0.00005081968,0.00008597255,0.00006248919],"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":[0.00122164,0.0006501335,0.007260229,0.0008906076,0.0001493756,0.0008279366,0.0004338027,0.051628,0.08333607,0.01298717,0.120146,0.720469],"study_design_scores_gemma":[0.0003791233,0.000753623,0.005736125,0.00007439106,0.0001523461,0.001018181,0.0001364708,0.7358325,0.0624296,0.004270087,0.1890164,0.0002010952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07522322,0.001005852,0.7396014,0.000664682,0.0005363043,0.0004872658,0.001886626,0.1476186,0.03297611],"genre_scores_gemma":[0.7438176,0.0004728057,0.2218406,0.0008112627,0.0002593846,0.0004352611,0.004391818,0.0006510311,0.02732023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007957225,"threshold_uncertainty_score":0.02661955,"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."}}