{"id":"W4377970556","doi":"10.1109/iccci56745.2023.10128340","title":"Sensor Based Sewage Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Municipal Solid Waste Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Garbage; Reuse; Waste management; Process (computing); Municipal solid waste; Computer science; Environmental science; Construction waste; Process engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001664544,0.0008411626,0.000912106,0.001360273,0.0004659274,0.00134956,0.0006186612,0.001480599,0.008604676],"category_scores_gemma":[0.0003816305,0.0004386653,0.0006111676,0.001189448,0.0002527842,0.0008153159,0.0006873306,0.0004476821,0.005960699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006589282,"about_ca_system_score_gemma":0.0007092911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003785616,"about_ca_topic_score_gemma":0.006821222,"domain_scores_codex":[0.9997134,0.0000275718,0.00001406327,0.0001037674,0.00008297562,0.00005830274],"domain_scores_gemma":[0.9998635,0.00002105698,0.00001535358,0.00001921785,0.00006630611,0.00001453269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001160251,0.0002446278,0.009764468,0.0007091252,0.0001133691,0.0006026108,0.0002267453,0.1090538,0.2139351,0.003791506,0.02754542,0.6328529],"study_design_scores_gemma":[0.00004421638,0.0001797376,0.01136913,0.0000724909,0.00008470189,0.0004348094,0.0002450961,0.8196511,0.1206156,0.004391286,0.04283577,0.00007610912],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1204711,0.002214663,0.783332,0.0008629463,0.0009393641,0.0003578154,0.007647417,0.02544617,0.05872852],"genre_scores_gemma":[0.6833692,0.001266567,0.263662,0.0005810279,0.00016556,0.0002066778,0.008380793,0.0008768408,0.04149145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008604676,"threshold_uncertainty_score":0.02878553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033926557697641,"score_gpt":0.2544841733651604,"score_spread":0.234144907788184,"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."}}