{"id":"W4379875209","doi":"10.1109/icaaic56838.2023.10141483","title":"Automated Garbage Classification using Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Garbage; Computer science; Sorting; Process (computing); Task (project management); Deep learning; Artificial intelligence; Bottleneck; Contextual image classification; Automation; Waste management; Engineering; Image (mathematics); Embedded system; Systems engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001007259,0.00007537453,0.00006880389,0.0001346221,0.00005849968,0.00003297481,0.0000390097,0.00006868879,0.0001130241],"category_scores_gemma":[0.00002132577,0.00008280135,0.00002229647,0.0004511947,0.000006943366,0.0001323261,0.00001068383,0.0001141336,0.001392909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005572576,"about_ca_system_score_gemma":0.000003284281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006323343,"about_ca_topic_score_gemma":0.000004150905,"domain_scores_codex":[0.9994861,0.00001806637,0.0001230993,0.00009845506,0.00008815221,0.000186068],"domain_scores_gemma":[0.9998118,0.00003765615,0.00001469596,0.00007511846,0.00002316611,0.00003755596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002552827,0.000007364246,0.001296201,0.00007842811,0.00004124825,0.00001616655,0.0003265064,0.456223,0.4981726,0.0001621739,0.0006666643,0.04300717],"study_design_scores_gemma":[0.00009039692,0.000003695789,0.01354275,0.00001175356,0.000007256253,0.000007086151,0.0001607316,0.9814556,0.004039054,0.00002654402,0.0005507066,0.0001044242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661868,0.00001499579,0.006917676,0.00001750578,0.0001511269,0.0000725635,7.548701e-7,0.01223259,0.01440603],"genre_scores_gemma":[0.9984501,0.00003573434,0.001207773,0.000007618557,0.00004222816,0.000005707793,0.0000512611,0.00003545066,0.000164105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5252326,"threshold_uncertainty_score":0.9993846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0355452362645881,"score_gpt":0.2574263930201213,"score_spread":0.2218811567555332,"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."}}