{"id":"W2176941056","doi":"10.24908/pceea.v0i0.3950","title":"KNOWLEDGE-BASED ROBUST PIPING DESIGN","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Piping; Procurement; Design cycle; Expert system; Computer science; Risk analysis (engineering); Order (exchange); Systems engineering; Engineering; Reliability engineering; Artificial intelligence; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0009937192,0.0007286793,0.000727482,0.0006733249,0.0004282689,0.001244961,0.001282481,0.001170089,0.004849067],"category_scores_gemma":[0.002570468,0.000493625,0.0008650783,0.0003510162,0.0008308764,0.001083785,0.001370985,0.000658806,0.0007876761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008781265,"about_ca_system_score_gemma":0.001355925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002737543,"about_ca_topic_score_gemma":0.002458392,"domain_scores_codex":[0.9990309,0.0002065981,0.0000637373,0.0002516404,0.0003723478,0.00007476161],"domain_scores_gemma":[0.9992742,0.0002912684,0.00006946958,0.0001438264,0.0002010654,0.00002014726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005975057,0.00005109216,0.0002116663,0.0001232854,0.00003245529,0.00009818544,0.00008977426,0.8691105,0.008540153,0.01990149,0.0006907336,0.101091],"study_design_scores_gemma":[0.00001462924,0.00004414061,0.000110822,0.00001458699,0.00001746778,0.00003068025,0.0000172521,0.981847,0.002922441,0.01269075,0.002280335,0.00001003339],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00554318,0.0000661814,0.989018,0.00006577392,0.00001029485,0.00004671335,0.00003646445,0.0002989513,0.004914518],"genre_scores_gemma":[0.4318732,0.0002587391,0.5612553,0.0001020424,0.0000214477,0.0003371135,0.0002764034,0.0001087406,0.00576695],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004849067,"threshold_uncertainty_score":0.01622176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939044559624413,"score_gpt":0.1836034090353399,"score_spread":0.1642129634390957,"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."}}