{"id":"W3099548931","doi":"10.3390/ma13225236","title":"Numerical Modeling and Analysis of Ti6Al4V Alloy Chip for Biomedical Applications","year":2020,"lang":"en","type":"article","venue":"Materials","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"King Saud University","keywords":"Titanium alloy; Materials science; Machining; Chip; Shearing (physics); Chip formation; Flow stress; Mechanical engineering; Macro; Alloy; Structural engineering; Tool wear; Composite material; Metallurgy; Computer science; Engineering","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":[0.0002573838,0.0003314906,0.0003377612,0.0003483214,0.0002736202,0.0005904509,0.0007229656,0.00076048,0.001535114],"category_scores_gemma":[0.0004981945,0.0002667555,0.000395667,0.0002806909,0.0003590699,0.0002181982,0.0002983192,0.0002156237,0.0003451606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005462405,"about_ca_system_score_gemma":0.0009431855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003602264,"about_ca_topic_score_gemma":0.002858056,"domain_scores_codex":[0.9998741,0.0000193025,0.000005895136,0.0000131673,0.00007066663,0.00001688021],"domain_scores_gemma":[0.9998614,0.00005036332,0.00002218453,0.00001780351,0.00003973586,0.00000861692],"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":[0.00001294352,0.00001109764,0.0003637908,0.00004379969,0.000006864551,0.00005885591,0.00002958538,0.9788608,0.01247881,0.002940216,0.000196674,0.004996612],"study_design_scores_gemma":[0.000001865045,0.00001040202,0.0002073662,0.000002976028,0.000002095244,0.00001587311,0.000006222328,0.9975153,0.001106472,0.0003073457,0.0008209985,0.000002990253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2369212,0.001287862,0.7272722,0.000395599,0.0001547974,0.0001560664,0.0004612621,0.0008129149,0.03253819],"genre_scores_gemma":[0.8817841,0.0006480346,0.1073534,0.0000581561,0.0000249346,0.0002294181,0.0002367291,0.0001001952,0.00956505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003602264,"threshold_uncertainty_score":0.007162571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01637101307095152,"score_gpt":0.2573801494688799,"score_spread":0.2410091363979284,"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."}}