{"id":"W4402059908","doi":"10.1061/9780784485583.038","title":"AI-Influenced Condition Assessment Analyses for Toronto Trunk Sewers","year":2024,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sanitary sewer; Computer science; Engineering; Environmental engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00103631,0.0005036339,0.0003028561,0.002360287,0.0007801507,0.001308379,0.0004581198,0.0004698988,0.002755103],"category_scores_gemma":[0.00431677,0.000211763,0.0003594182,0.001294705,0.0006949963,0.0004244472,0.0006586127,0.0003553229,0.0003752518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003645722,"about_ca_system_score_gemma":0.00219475,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3328581,"about_ca_topic_score_gemma":0.4863473,"domain_scores_codex":[0.9992575,0.00009630883,0.00005452634,0.0001341756,0.0003662749,0.00009109811],"domain_scores_gemma":[0.9979395,0.0006351076,0.0001747456,0.0001304671,0.001045575,0.00007448816],"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":[0.001030184,0.0002785185,0.2662711,0.0004135087,0.000117159,0.001169693,0.00329923,0.4424115,0.08662206,0.003840897,0.003724557,0.1908216],"study_design_scores_gemma":[0.00002502657,0.0002083424,0.2606598,0.00004500382,0.00006672487,0.0001143002,0.002127801,0.6977539,0.03383589,0.001099509,0.003985981,0.0000777316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596079,0.00009313396,0.03142357,0.00007135811,0.00001834313,0.0001945198,0.001466751,0.0004984929,0.006626091],"genre_scores_gemma":[0.9852158,0.00004783403,0.01213799,0.00000730317,0.000002520185,0.00004214878,0.0007331995,0.00002774233,0.001785417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6671419,"threshold_uncertainty_score":0.6618412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341244092645822,"score_gpt":0.3649184454322221,"score_spread":0.3515060045057639,"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."}}