{"id":"W4406563846","doi":"10.1016/j.rineng.2025.104055","title":"Optimisation of trimethylolpropane ester synthesis from waste cooking oil methyl ester by response surface methodology, and its physicochemical properties and tribological characteristics","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Lubricants and Their Additives","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Mechanical Engineering, University of Alberta; Faculty of Engineering and Information Technology, University of Technology Sydney; University of Technology Sydney","keywords":"Trimethylolpropane; Tribology; Response surface methodology; Materials science; Chemical engineering; Cooking oil; Organic chemistry; Composite material; Chemistry; Chromatography; Engineering; Biodiesel; Catalysis","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":[],"consensus_categories":[],"category_scores_codex":[0.000614615,0.0002079195,0.0004633038,0.000109482,0.00001952894,0.00002644274,0.00009228042,0.0001485852,0.000002400183],"category_scores_gemma":[0.001370994,0.0001791356,0.00003071874,0.0001634312,0.00005508356,0.0001118918,0.00007076775,0.0002188654,4.361689e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004523759,"about_ca_system_score_gemma":0.00001151495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001456951,"about_ca_topic_score_gemma":3.790173e-7,"domain_scores_codex":[0.9988716,0.0001545156,0.0004068267,0.0002614133,0.0000876401,0.0002179713],"domain_scores_gemma":[0.997737,0.001995945,0.00005066121,0.0001395458,0.00002940704,0.00004747881],"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":[0.0005536898,0.00002082487,0.00003770374,0.0001884468,0.00007371681,0.000003883327,0.0005098516,0.005927377,0.9840124,0.00001334969,0.00001729261,0.00864148],"study_design_scores_gemma":[0.000673058,0.00002554265,0.001470076,0.0004854481,0.00003964101,0.000002169902,0.0001249274,0.07157101,0.9251713,0.000008711073,0.0002244736,0.000203662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995355,0.002297071,0.001697302,0.00007715681,0.0001019176,0.00009224385,0.0002350246,0.00007852079,0.00006574375],"genre_scores_gemma":[0.9954008,0.0006262898,0.003818667,0.000008523606,0.00002856968,0.0000167721,0.00001544998,0.00002277772,0.00006215907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06564363,"threshold_uncertainty_score":0.7304938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03052448314420984,"score_gpt":0.2381267087345945,"score_spread":0.2076022255903847,"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."}}