{"id":"W2943114411","doi":"10.1016/j.triboint.2019.04.039","title":"New observations on built-up edge structures for improving machining performance during the cutting of superduplex stainless steel","year":2019,"lang":"en","type":"article","venue":"Tribology International","topic":"Hydrogen embrittlement and corrosion behaviors in metals","field":"Materials Science","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Electron backscatter diffraction; Machining; Scanning electron microscope; Metallurgy; Enhanced Data Rates for GSM Evolution; Surface integrity; Austenitic stainless steel; Ferrite (magnet); Composite material; Microstructure; Computer 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":[0.0001037516,0.0002280653,0.0001352585,0.0001965804,0.0002085619,0.0002736541,0.0003973981,0.0004352383,0.001349312],"category_scores_gemma":[0.000229938,0.0001703904,0.0001072055,0.0001508439,0.0003676207,0.0003351354,0.0001929674,0.000499158,0.0002304046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001476325,"about_ca_system_score_gemma":0.00008422391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000403464,"about_ca_topic_score_gemma":0.0007760412,"domain_scores_codex":[0.9999079,0.000004779914,0.000002725007,0.00001446201,0.00004655624,0.00002352036],"domain_scores_gemma":[0.9998134,0.00004001765,0.00003680002,0.00003197485,0.00005780226,0.000020056],"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":[0.0000626334,0.000009937545,0.0002764346,0.00002497877,0.000001109144,0.00006961062,0.00006135452,0.00008067484,0.9973783,0.0001121096,0.00008937447,0.001833461],"study_design_scores_gemma":[0.000008717183,0.0001665966,0.008742752,0.000003666585,0.000004040752,0.0001379975,0.0001255547,0.001393837,0.9876646,0.00007168868,0.001673487,0.000007130816],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917079,0.000389716,0.005383172,0.00006630977,0.00002849844,0.00000886568,0.00008314302,0.0001708245,0.002161663],"genre_scores_gemma":[0.9955711,0.0001059798,0.002757358,0.00002511332,0.00000813482,0.000004883292,0.00007545167,0.00002653695,0.001425514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001349312,"threshold_uncertainty_score":0.00451386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03241509948787925,"score_gpt":0.296521067236956,"score_spread":0.2641059677490767,"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."}}