{"id":"W4413228279","doi":"10.18280/isi.300610","title":"Real Time Classification of Retail Theft Utilizing YOLO Algorithm","year":2025,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Business; Algorithm; Artificial intelligence","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":[0.0006761915,0.0003136819,0.000425036,0.0005680982,0.000180554,0.0001666297,0.000219072,0.0004920376,0.0002709005],"category_scores_gemma":[0.0002026847,0.0003807544,0.0001437119,0.0009142882,0.0002175828,0.003314359,0.00006596632,0.0003190914,0.0007756262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008994224,"about_ca_system_score_gemma":0.0001684776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003790384,"about_ca_topic_score_gemma":0.00001043997,"domain_scores_codex":[0.9976754,0.0001282567,0.00127969,0.0001717543,0.0002965838,0.0004482909],"domain_scores_gemma":[0.9984074,0.0001901909,0.0004457762,0.0003674166,0.0005077637,0.00008147578],"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":[0.00003203108,0.00003734535,0.0003111329,0.002407427,0.000205903,0.00000171348,0.004580467,0.001586823,0.009971206,0.01003156,0.0008948704,0.9699395],"study_design_scores_gemma":[0.0008464649,0.00009163643,0.02093585,0.002624574,0.0002404176,0.00004387673,0.002891698,0.9344108,0.01895042,0.006702635,0.0117523,0.0005093674],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4794475,0.001830059,0.1208853,0.0005124921,0.002872289,0.001186082,0.0003861304,0.0008819747,0.3919982],"genre_scores_gemma":[0.968185,0.001706876,0.0269505,0.00004712207,0.0001773204,0.0000769499,0.0007952439,0.00005000088,0.002010983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9694301,"threshold_uncertainty_score":0.9998645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655331091506265,"score_gpt":0.2304747062278643,"score_spread":0.2139213953128016,"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."}}