{"id":"W4401244789","doi":"10.1016/j.aej.2024.07.066","title":"Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development","year":2024,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Garbage; Sustainable development; Computer science; Artificial intelligence; Residual neural network; Pattern recognition (psychology); Development (topology); Environmental science; Agricultural engineering; Machine learning; Mathematics; Engineering; Deep learning; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000349514,0.0001702011,0.0001858818,0.0003707802,0.00007901893,0.0001220203,0.00005893514,0.0001009872,0.00002170911],"category_scores_gemma":[0.00005382831,0.0001709329,0.0000611228,0.0001866698,0.000007103647,0.0002007572,0.000007053065,0.0001770926,0.000002520187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001498636,"about_ca_system_score_gemma":0.00006199654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001457208,"about_ca_topic_score_gemma":3.945863e-7,"domain_scores_codex":[0.9990668,0.00001308405,0.0003774951,0.0001365864,0.0001562168,0.000249828],"domain_scores_gemma":[0.9996032,0.00008079616,0.00002938109,0.00008513968,0.0001035158,0.00009797752],"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":[0.00003260398,0.000009157495,0.000003629825,0.0006987741,0.00004932249,0.000009175378,0.0002482637,0.9933176,0.003017826,0.00004285047,0.001393358,0.001177449],"study_design_scores_gemma":[0.0005533442,0.00007104802,0.00005170922,0.0004440278,0.00002289724,0.00003190638,0.00004718821,0.992089,0.003627287,0.000007986022,0.00286147,0.000192105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03566174,0.0005516476,0.961496,0.00003505027,0.0005679391,0.0003222403,0.00001157955,0.0008933658,0.0004604308],"genre_scores_gemma":[0.9536335,0.00006097385,0.04575019,0.00001034314,0.0001266414,0.00004195366,0.00003276162,0.00007599966,0.0002676589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9179717,"threshold_uncertainty_score":0.6970441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009260302313678038,"score_gpt":0.1943468564685202,"score_spread":0.1850865541548422,"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."}}