{"id":"W4411341629","doi":"10.1007/s10586-024-05088-w","title":"Aqua garbage collector: utilizing AI and IoT for efficient underwater garbage classification","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Garbage; Internet of Things; Garbage collection; Underwater; Artificial intelligence; Embedded system; Real-time computing; Programming language; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002524668,0.0007408967,0.000557504,0.001157188,0.0005862447,0.0009133506,0.001061773,0.000415291,0.002588405],"category_scores_gemma":[0.0004523924,0.0002420818,0.0003726679,0.001315165,0.0002551163,0.001056171,0.0008255324,0.0004239041,0.0009006895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004842027,"about_ca_system_score_gemma":0.000859914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005880054,"about_ca_topic_score_gemma":0.01249432,"domain_scores_codex":[0.9998134,0.00001553491,0.00001186672,0.00005582653,0.00007216908,0.00003110253],"domain_scores_gemma":[0.9997588,0.00005473107,0.00002353139,0.00004558375,0.00008774514,0.00002968438],"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":[0.001129002,0.000438714,0.01511232,0.0004706706,0.0002050059,0.0004462394,0.0003793833,0.03294053,0.1562548,0.004023661,0.02514189,0.7634578],"study_design_scores_gemma":[0.00004160085,0.000248991,0.007688338,0.00005230934,0.0001082068,0.0002553485,0.0003943495,0.8699095,0.09555952,0.004988057,0.02067752,0.00007622355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1836195,0.002003824,0.7466677,0.0007277154,0.0006240129,0.0003476631,0.002021942,0.04474325,0.0192444],"genre_scores_gemma":[0.6847927,0.0007741555,0.2987098,0.0004045101,0.000113235,0.0001733784,0.002014095,0.0005227851,0.01249536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005880054,"threshold_uncertainty_score":0.01169169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03608731604708054,"score_gpt":0.2960237010436957,"score_spread":0.2599363849966151,"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."}}