{"id":"W4390271364","doi":"10.18280/ria.370604","title":"Integration of the Faster R-CNN Algorithm for Waste Detection in an Android Application","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Android (operating system); Embedded system; Android application; Algorithm; Real-time computing; Operating 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003522901,0.0007046887,0.0003159513,0.0007810039,0.000164159,0.0005079304,0.0008714744,0.0004557438,0.003439946],"category_scores_gemma":[0.001094455,0.0002102681,0.0003698363,0.0002959963,0.0001507204,0.0005326617,0.0004043225,0.0003311135,0.0008661572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004309474,"about_ca_system_score_gemma":0.0005548904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007193822,"about_ca_topic_score_gemma":0.0109697,"domain_scores_codex":[0.9997361,0.00002674767,0.00001679433,0.00006661069,0.000103149,0.00005054138],"domain_scores_gemma":[0.9997074,0.00007271465,0.00002543203,0.00004837856,0.0001331061,0.0000129339],"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":[0.0007641219,0.0002171485,0.005309316,0.0003482107,0.0001200851,0.001039596,0.0001941533,0.03030265,0.1609162,0.001988342,0.008512666,0.7902877],"study_design_scores_gemma":[0.00005150926,0.0003848097,0.007284624,0.00006025599,0.0001002956,0.0006852089,0.00009515783,0.8205631,0.155676,0.0008811919,0.01415047,0.00006727198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3955594,0.001430889,0.5510704,0.000668201,0.000564913,0.0005780096,0.0006988749,0.02543261,0.02399665],"genre_scores_gemma":[0.7059991,0.0005499149,0.2830882,0.0003311166,0.0000475482,0.000224543,0.0005071474,0.0003189933,0.008933494],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007193822,"threshold_uncertainty_score":0.01430392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02952864710059004,"score_gpt":0.2873394566529167,"score_spread":0.2578108095523266,"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."}}