{"id":"W1981215071","doi":"10.4271/2015-01-0684","title":"Boosting the Friction Performance of Amine Friction Modifiers with MoDTC","year":2015,"lang":"en","type":"article","venue":"SAE international journal of fuels and lubricants","topic":"Lubricants and Their Additives","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"AkzoNobel (Canada)","funders":"","keywords":"Boosting (machine learning); Materials science; Automotive engineering; Amine gas treating; Friction modifier; Metallurgy; Forensic engineering; Composite material; Environmental science; Computer science; Engineering; Artificial intelligence; Lubricant; Environmental 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001925698,0.00008211931,0.0001266625,0.0001024144,0.00003278246,0.00002737452,0.0001446032,0.00002179702,0.000009748286],"category_scores_gemma":[0.00002717698,0.00004943682,0.00003040399,0.00009648877,0.00005214191,0.0002838652,0.00001857293,0.0001270786,9.035626e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005441254,"about_ca_system_score_gemma":0.00002877984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002719246,"about_ca_topic_score_gemma":0.000004596355,"domain_scores_codex":[0.9992568,0.00001536008,0.0002415787,0.00005482218,0.0003386147,0.00009283998],"domain_scores_gemma":[0.9993456,0.00004697187,0.0001777005,0.00005038667,0.000314866,0.00006447682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001997343,0.0004336405,0.1619951,0.0001759519,0.00358157,0.0001715004,0.01398607,0.1709552,0.04086216,0.001822865,0.00838373,0.5956349],"study_design_scores_gemma":[0.007710314,0.00278165,0.7136773,0.001346032,0.0002960369,0.003658176,0.009151939,0.205873,0.03483254,0.002011402,0.01775426,0.0009074261],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994115,0.000384038,0.001918414,0.000137485,0.0004980802,0.00003821485,0.0000130429,0.00001120301,0.002884544],"genre_scores_gemma":[0.9989406,0.0004888963,0.0002691579,0.00002198095,0.0002261129,0.000001241881,0.00000158544,0.000009935066,0.00004045654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5947275,"threshold_uncertainty_score":0.2015975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535571947510351,"score_gpt":0.2179867677428815,"score_spread":0.202631048267778,"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."}}