{"id":"W2393219631","doi":"10.1016/j.measurement.2016.05.001","title":"Prioritizing deterioration factors of water pipelines using Delphi method","year":2016,"lang":"en","type":"article","venue":"Measurement","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Delphi method; Pipeline (software); Construct (python library); Set (abstract data type); Rank (graph theory); Delphi; Risk analysis (engineering); Computer science; Engineering; Operations research; Forensic engineering; Mathematics; Business; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005644496,0.0001186355,0.0001371848,0.00001970313,0.00009173306,0.0000113771,0.00009943038,0.00004261723,0.0004835251],"category_scores_gemma":[0.00002506416,0.00006483662,0.00005893149,0.0000451562,0.00006601565,0.0002509147,0.00009970342,0.00002919398,0.00005047965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005249733,"about_ca_system_score_gemma":0.000006035543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002997244,"about_ca_topic_score_gemma":0.00006558651,"domain_scores_codex":[0.9986401,0.00009070331,0.0002412018,0.000175421,0.0006276703,0.0002249286],"domain_scores_gemma":[0.9997013,0.00001230654,0.00007324148,0.0001330981,0.00001112628,0.00006898138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005195091,0.00004171998,0.1018582,0.000003562388,0.000008709051,4.89803e-7,0.0004410671,0.00001807857,0.8910828,0.000001774847,0.00001361978,0.006524822],"study_design_scores_gemma":[0.0003584253,0.00007069707,0.1951865,0.00004291099,0.0000313736,0.000001176196,0.0002414054,0.0000815461,0.8028536,0.0002896566,0.0006602964,0.0001823959],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970675,0.00001027671,0.02854488,0.00008547027,0.0001246781,0.000146946,0.000003334037,0.00001437122,0.0003950386],"genre_scores_gemma":[0.9934953,0.000005518922,0.00635929,0.00003338844,0.0000302257,0.000003710105,0.000001054245,0.00001192231,0.00005959228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09332839,"threshold_uncertainty_score":0.5294261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09046211404576696,"score_gpt":0.3168071235415886,"score_spread":0.2263450094958216,"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."}}