{"id":"W4367016913","doi":"10.2139/ssrn.4427914","title":"Semi-Surrogate Modelling of Droplets Evaporation Process Via XGBoost Integrated CFD Simulations","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computational fluid dynamics; Evaporation; Process (computing); Environmental science; Surrogate model; Mechanics; Computer science; Thermodynamics; Physics; Machine learning","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000857861,0.0003584588,0.0004626135,0.000547749,0.0001481921,0.00007167413,0.0004472859,0.0004717401,0.000004864745],"category_scores_gemma":[0.0001006178,0.000349514,0.0001516105,0.0004603154,0.00004846894,0.0001330709,0.00009670846,0.00469188,0.00001700556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009102586,"about_ca_system_score_gemma":0.001080367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001373801,"about_ca_topic_score_gemma":0.0004096908,"domain_scores_codex":[0.9973123,0.00004345645,0.0006823523,0.0002782714,0.0003434585,0.001340178],"domain_scores_gemma":[0.9988756,0.0000752775,0.0003102772,0.0003599747,0.0003334581,0.00004543457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001170607,0.00001590028,0.0001265105,0.0001447527,0.0002228744,0.000002716028,0.0003018429,0.992947,0.0003963654,0.000973939,0.00002728268,0.004829075],"study_design_scores_gemma":[0.0001744452,0.00005422669,0.000009110053,0.0003441753,0.00007505393,0.00003503316,0.0004815469,0.752149,0.005641441,0.2407658,0.00001402253,0.0002561888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2948334,0.0005077249,0.7027714,0.00009690459,0.0004776053,0.0001881829,0.00002209591,0.001025424,0.00007723798],"genre_scores_gemma":[0.9969854,0.00201459,0.0004530729,0.000003591806,0.0001351154,0.0000125502,0.0001153214,0.0001117246,0.0001686227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7023183,"threshold_uncertainty_score":0.9998957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723174170888453,"score_gpt":0.255179568088197,"score_spread":0.2279478263793125,"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."}}