{"id":"W2969562078","doi":"10.11575/prism/36740","title":"Enhancing Tribological Properties of Metallic Sliding Surfaces through Micro Multi-texturing Techniques","year":2019,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Tribology and Lubrication Engineering","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Consejo Nacional de Ciencia y Tecnología","keywords":"Tribology; Materials science; Metal; Composite material; Metallurgy; Nanotechnology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001360218,0.0002528765,0.0005874033,0.0002099311,0.00007115304,0.000006191261,0.0003428488,0.0004838564,0.00005036526],"category_scores_gemma":[0.00002862912,0.0002888172,0.0001820997,0.0001582975,0.00004841994,0.0002358748,0.0000434769,0.0004098222,0.00001250992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006237532,"about_ca_system_score_gemma":0.00003688699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001264712,"about_ca_topic_score_gemma":0.00001549393,"domain_scores_codex":[0.9990708,0.00003643775,0.0002754673,0.0002445553,0.000149504,0.0002232723],"domain_scores_gemma":[0.9994236,0.00005024829,0.0001845854,0.0002259361,0.00008019536,0.0000354455],"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":[0.00005421968,0.00004634438,0.000106361,0.001659492,0.0002644761,0.000007189143,0.004145008,0.0001441265,0.9874418,0.0002584155,0.00003613372,0.005836431],"study_design_scores_gemma":[0.0003444029,0.00004010818,0.002386994,0.0005484502,0.0001651314,0.000003040237,0.0007571688,0.009850805,0.9835541,0.00002499713,0.001924818,0.000400041],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8628921,0.004795769,0.1293079,0.000009224837,0.0002810695,0.0003596368,0.000001381086,0.0003562177,0.001996756],"genre_scores_gemma":[0.9278217,0.001129535,0.06846467,0.000002667558,0.000009888343,0.000001915859,0.00007777794,0.0000410947,0.00245076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0649296,"threshold_uncertainty_score":0.9999564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616653804275644,"score_gpt":0.2024382037089903,"score_spread":0.1862716656662338,"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."}}