{"id":"W2341114807","doi":"10.3390/lubricants4020010","title":"Influence of Workpiece Material on Tool Wear Performance and Tribofilm Formation in Machining Hardened Steel","year":2016,"lang":"en","type":"article","venue":"Lubricants","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Machining; Metallurgy; Scanning electron microscope; Ceramic; Tool wear; Hardened steel; Carbide; Tribology; X-ray photoelectron spectroscopy; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006565138,0.00008171875,0.0001141918,0.00007242732,0.0000224149,0.00001003937,0.00005623054,0.00003426708,0.000007936187],"category_scores_gemma":[0.00002903941,0.00006392901,0.000007587142,0.0001072915,0.00001627697,0.0003764327,0.00001652613,0.00004390118,0.000003862816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002989148,"about_ca_system_score_gemma":0.000004457637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003035562,"about_ca_topic_score_gemma":0.000002128424,"domain_scores_codex":[0.9994984,0.000008244404,0.0001872232,0.00008965186,0.00008419089,0.0001323396],"domain_scores_gemma":[0.9998017,0.00003327103,0.00004945272,0.00008005093,0.00001560719,0.00001989585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001679056,0.00001345223,0.007712234,0.0001753994,0.000006190831,0.000001730643,0.0004376104,0.8780423,0.02228783,0.0001018678,0.00002013041,0.0910333],"study_design_scores_gemma":[0.004446822,0.0004422123,0.3761423,0.00246711,0.00002069586,0.00003131156,0.0001146347,0.3058553,0.3086073,0.0002970656,0.0007143448,0.0008608892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941333,0.00001938619,0.005310419,0.00001659654,0.00005638592,0.00007901049,0.00001099827,0.00005561602,0.0003183037],"genre_scores_gemma":[0.9979377,0.0003301279,0.001642864,0.00001494594,0.00001384554,0.000009263005,0.000002919341,0.00001292507,0.00003536479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.572187,"threshold_uncertainty_score":0.2606949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005706718111887447,"score_gpt":0.2026827203124474,"score_spread":0.1969760022005599,"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."}}