{"id":"W2900032200","doi":"10.1115/ipc2018-78431","title":"Coupling Metallurgy and Manufacturing Parameters of Pipeline Fittings to Avoid Substandard Properties","year":2018,"lang":"en","type":"article","venue":"Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining","topic":"Microstructure and Mechanical Properties of Steels","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); McMaster University","funders":"Natural Resources Canada","keywords":"Welding; Engineering; Pipeline (software); Tempering; Upgrade; Mechanical engineering; Pipeline transport; Rigidity (electromagnetism); Manufacturing engineering; Process engineering; Computer science; Structural engineering; Metallurgy; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005485161,0.0005586423,0.0004427284,0.0005405113,0.0003103799,0.0006249487,0.0006103901,0.0004958017,0.003172413],"category_scores_gemma":[0.00162143,0.0003653059,0.0004392913,0.0005557723,0.0003073053,0.0006102447,0.0003119837,0.0005276333,0.001105743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005124469,"about_ca_system_score_gemma":0.0004401556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002504995,"about_ca_topic_score_gemma":0.004904705,"domain_scores_codex":[0.9994722,0.00003408994,0.00003940294,0.000127714,0.0002779816,0.00004858733],"domain_scores_gemma":[0.9993717,0.000136965,0.0001252552,0.0001205511,0.0002203188,0.00002535371],"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":[0.0005138076,0.0001449898,0.01843095,0.0008262126,0.00004103565,0.0003525609,0.0005225516,0.04984389,0.8865081,0.0004429907,0.001437952,0.04093491],"study_design_scores_gemma":[0.00002638771,0.001105549,0.08049612,0.00005702562,0.0001075319,0.0003555516,0.0004001766,0.07353979,0.8305334,0.0004610074,0.0128035,0.0001138811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.957552,0.0005135744,0.03373858,0.00009706629,0.00004919948,0.000172015,0.001519718,0.001437071,0.004920889],"genre_scores_gemma":[0.986698,0.0001837129,0.01077837,0.0000196277,0.000004438483,0.00005346645,0.0006371799,0.0001615724,0.001463583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003172413,"threshold_uncertainty_score":0.01061285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01480702203482196,"score_gpt":0.2105112630100017,"score_spread":0.1957042409751798,"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."}}