{"id":"W4310191910","doi":"10.18280/isi.270514","title":"A Smart Car Parking System Based on IoT with Gray Wolf Optimization-Probability Correlated Neural Network Recognition Methods","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Internet of Things; Artificial neural network; Traffic congestion; Real-time computing; Artificial intelligence; Parking lot; Intelligent transportation system; Data mining; Transport engineering; Engineering; Embedded system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002378371,0.0003265109,0.0003871955,0.0004225247,0.0008014546,0.0002616897,0.0002691415,0.0001311826,0.0001038906],"category_scores_gemma":[0.0001944706,0.0003242738,0.00009440949,0.001468368,0.00007520399,0.0006935946,0.00006847224,0.0005581287,0.00003347218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001881478,"about_ca_system_score_gemma":0.0001075212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169671,"about_ca_topic_score_gemma":0.000009442409,"domain_scores_codex":[0.9967875,0.0008176176,0.0008647889,0.0002428075,0.0007012357,0.0005860641],"domain_scores_gemma":[0.9984522,0.0003778881,0.0002713122,0.0004763363,0.0002928388,0.0001294291],"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":[0.0001522689,0.00001368799,0.00307307,0.0006790852,0.0000581458,0.000005195239,0.0009330063,0.9820767,0.00001682481,0.00006168409,0.0002940127,0.01263632],"study_design_scores_gemma":[0.0006555114,0.0002229118,0.001092153,0.000367089,0.00003129098,0.00007749306,0.0007026066,0.9949125,0.0001273085,0.00005646174,0.001387227,0.0003674453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2496798,0.0001272223,0.7273477,0.00002386952,0.002600548,0.002455211,0.0001024668,0.002468048,0.01519515],"genre_scores_gemma":[0.9681287,0.000001530569,0.03022479,0.00005546357,0.0001049694,0.000917276,0.0004946407,0.00006125833,0.000011404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7184489,"threshold_uncertainty_score":0.9999209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207028014627862,"score_gpt":0.2434905774901755,"score_spread":0.2214202973438968,"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."}}