{"id":"W3097491851","doi":"10.3390/ma13215011","title":"Machining of Titanium Metal Matrix Composites: Progress Overview","year":2020,"lang":"en","type":"review","venue":"Materials","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Polytechnique Montréal","funders":"","keywords":"Machining; Materials science; Tool wear; Grinding; Surface roughness; Chip formation; Adiabatic shear band; Titanium alloy; Metallurgy; Surface finish; Lubrication; Titanium; Composite material; Cutting tool; Shear (geology); Alloy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001154565,0.0003656029,0.001745128,0.0000792209,0.00002887352,0.00006117801,0.0002471909,0.000162729,0.0002201953],"category_scores_gemma":[0.00001620716,0.0003127224,0.0001611965,0.0002025262,0.00002430025,0.0001018689,0.00008600172,0.0001222485,0.00006398036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004107432,"about_ca_system_score_gemma":0.00002965506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001724691,"about_ca_topic_score_gemma":1.645108e-7,"domain_scores_codex":[0.9986874,0.00005810952,0.0007114318,0.0002172187,0.0001364421,0.0001894053],"domain_scores_gemma":[0.9993972,0.00004146892,0.0002845126,0.0001969699,0.00002786097,0.00005194797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000895033,0.00002490201,4.973213e-7,0.253935,0.0004888496,0.00002429571,0.00009321762,0.002805667,0.0002644595,0.001477396,0.0001489518,0.7407278],"study_design_scores_gemma":[0.0000940632,0.00003455313,1.663536e-7,0.00911946,0.0006302731,0.00002000511,0.000003492699,0.0003066587,0.0007536928,0.00002994473,0.9886342,0.0003734819],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000002180979,0.9955835,0.002470509,0.000003055753,0.0006301277,0.0004106676,0.0002646614,0.0002891369,0.0003461055],"genre_scores_gemma":[0.00006444289,0.9869372,0.01224225,0.000004395369,0.0001696554,0.0000624375,0.0003672853,0.0001299066,0.00002238391],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9884853,"threshold_uncertainty_score":0.9999325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0343419547927369,"score_gpt":0.3355540046326184,"score_spread":0.3012120498398815,"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."}}