{"id":"W2742226295","doi":"10.1061/9780784480885.056","title":"Using Risk Models and Automated Defect Characterization Algorithms to Convert PACP Data into Capital Upgrading Programs for ALCOSAN","year":2017,"lang":"en","type":"article","venue":"Pipelines 2017","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Manitoba","funders":"","keywords":"Sanitary sewer; Data collection; Process (computing); CLARITY; Task (project management); Computer science; Capital expenditure; Watershed; Engineering; Systems engineering; Business; Environmental engineering; Machine learning; Finance","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":[],"consensus_categories":[],"category_scores_codex":[0.0004088127,0.0002043498,0.0002482019,0.0001006461,0.0003520454,0.0003653493,0.0004249497,0.0001012574,0.000001212913],"category_scores_gemma":[0.0003222752,0.0002019852,0.00004709027,0.00005234312,0.0000278282,0.0007582167,0.0001480431,0.00009231603,0.000003521117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003017564,"about_ca_system_score_gemma":0.00001262325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001105735,"about_ca_topic_score_gemma":0.00001334143,"domain_scores_codex":[0.9990328,0.00002608052,0.0002492981,0.0003158121,0.0001193275,0.0002566937],"domain_scores_gemma":[0.9987275,0.0000595894,0.00009148165,0.0008936773,0.00008562693,0.0001421234],"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":[0.0000217213,0.00001775295,0.002832683,0.0002619966,0.0001084388,0.000003284289,0.0008851977,0.8935342,0.01770776,0.00004531031,0.0002276526,0.08435396],"study_design_scores_gemma":[0.0004556144,0.00001611303,0.002178578,0.00006496997,0.00004671889,0.000004134403,0.00002207874,0.9953895,0.0004449807,0.0002230158,0.0008972677,0.0002570384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4156826,0.0001241717,0.5829657,0.00002157816,0.0003876837,0.0003119491,0.00007094734,0.0004185685,0.00001690841],"genre_scores_gemma":[0.7833484,0.0001062039,0.2157948,0.000005491271,0.0002916635,0.00002908874,0.0003404287,0.00005644888,0.00002753241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3676658,"threshold_uncertainty_score":0.8236719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1482732324475632,"score_gpt":0.3802593711915498,"score_spread":0.2319861387439866,"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."}}