{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003132153,0.0001627173,0.000139724,0.0003058758,0.0001267105,0.00006690374,0.0002821543,0.0001525211,0.00006692964],"category_scores_gemma":[0.0002859863,0.0001655431,0.00006462665,0.0004310569,0.00001024405,0.0002129928,0.00001117501,0.0001837541,0.00001233457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347596,"about_ca_system_score_gemma":0.0003815364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001257228,"about_ca_topic_score_gemma":0.0009781005,"domain_scores_codex":[0.9991452,0.000004453057,0.0002453634,0.0001421783,0.0001682011,0.0002946331],"domain_scores_gemma":[0.9992099,0.00002965538,0.0001378537,0.00009141942,0.0003696501,0.0001614527],"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":[0.00000598816,0.0001199644,0.01882076,0.00135288,0.0001694907,1.146076e-7,0.004675453,0.927212,0.001080541,0.005141126,0.03738335,0.004038332],"study_design_scores_gemma":[0.0005267232,0.00003661895,0.09192578,0.0006165004,0.0001515024,0.000003154168,0.0002751779,0.7988747,0.08271653,0.0004507384,0.02340123,0.001021362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3685696,0.003733174,0.1874477,0.004937994,0.03342852,0.007223409,0.000136313,0.006094465,0.3884287],"genre_scores_gemma":[0.9882848,0.00001048151,0.01066013,0.00006244876,0.0001137418,0.00006349838,0.000004601695,0.00005380075,0.0007465383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6197151,"threshold_uncertainty_score":0.6750652,"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."}}