{"id":"W2098888815","doi":"10.1061/40934(252)18","title":"Sewer Pipeline Operational Condition Prediction Using Multiple Regression","year":2007,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline (software); Sanitary sewer; Regression analysis; Vulnerability (computing); Pipeline transport; Engineering; Reliability engineering; Computer science; Civil engineering; Environmental 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":[0.0005672706,0.0005936986,0.0006583864,0.0006446734,0.0001688191,0.0005671378,0.0005988428,0.0005200228,0.001331458],"category_scores_gemma":[0.003424041,0.0003205152,0.0004970934,0.0006590228,0.0002009712,0.0007165085,0.0002963696,0.0006514706,0.0003941446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006242754,"about_ca_system_score_gemma":0.0004679689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01120749,"about_ca_topic_score_gemma":0.008276348,"domain_scores_codex":[0.9995074,0.000134548,0.00002116674,0.0001677622,0.0001195356,0.00004953585],"domain_scores_gemma":[0.9990236,0.0005408124,0.000155432,0.00006977107,0.0001821877,0.00002826358],"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":[0.0000400412,0.00003597378,0.004729564,0.00001571188,0.00002404738,0.000026886,0.0000126392,0.9712459,0.001599041,0.0002733518,0.0002264649,0.02177031],"study_design_scores_gemma":[0.000001134593,0.00001060505,0.0006660428,7.034575e-7,0.000001782811,0.000003176223,0.000001662146,0.9987981,0.000393031,0.00007877029,0.00004259408,0.000002403702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3181484,0.0001336139,0.678144,0.0001218685,0.00001363462,0.00004212285,0.0003615104,0.001576821,0.001457931],"genre_scores_gemma":[0.969588,0.00006421787,0.02895346,0.000006564737,0.000005087365,0.00003217132,0.0002060802,0.00004149326,0.001102845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01120749,"threshold_uncertainty_score":0.02228451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01395006258528837,"score_gpt":0.229747540942069,"score_spread":0.2157974783567806,"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."}}