{"id":"W3083747520","doi":"10.1016/j.dib.2020.106275","title":"Industrial pipelines data generator","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pipeline transport; Generator (circuit theory); Computer science; Installation; Benchmark (surveying); Industrial engineering; Construction engineering; Engineering; Mechanical engineering; Power (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001499101,0.001397551,0.0007991684,0.001749029,0.0003777225,0.001107814,0.003219753,0.001247786,0.02623373],"category_scores_gemma":[0.009106521,0.000803071,0.001045294,0.002452739,0.000402008,0.001651738,0.001274471,0.001596208,0.01147004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071125,"about_ca_system_score_gemma":0.001519076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003311971,"about_ca_topic_score_gemma":0.002772721,"domain_scores_codex":[0.9990338,0.0001532673,0.000132819,0.0001877677,0.0004155972,0.00007673317],"domain_scores_gemma":[0.9965377,0.001099061,0.0001551774,0.0009454663,0.001127593,0.0001350799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001669493,0.000705295,0.01161917,0.002091282,0.0001732205,0.001263477,0.0004069315,0.1733503,0.01039491,0.03306642,0.5941616,0.1710979],"study_design_scores_gemma":[0.0007803212,0.0003152954,0.003434198,0.0001692503,0.00005732239,0.0005005833,0.0001239067,0.408313,0.0343109,0.02142395,0.5304652,0.0001060366],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.02719974,0.0003671371,0.3734562,0.000795585,0.0004332909,0.002569013,0.3989432,0.1693622,0.02687371],"genre_scores_gemma":[0.1594991,0.0004913421,0.2083419,0.0003938046,0.00006404734,0.00436587,0.6036153,0.01216269,0.01106601],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02623373,"threshold_uncertainty_score":0.08776057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1285382862701509,"score_gpt":0.2648676585038776,"score_spread":0.1363293722337267,"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."}}