{"id":"W4293116042","doi":"10.11159/htff22.164","title":"Determination of Void Fraction in Microchannel Flow Boiling Using Computer Vision","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Heat Transfer and Boiling Studies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Flow boiling; Microchannel; Boiling; Porosity; Computer science; Two-phase flow; Flow (mathematics); Slug flow; Fraction (chemistry); Mechanics; Materials science; Nucleate boiling; Thermodynamics; Chemistry; Composite material; Chromatography; Physics; Heat transfer","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.000188954,0.0001665794,0.0002976369,0.0001613546,0.0000675908,0.00002575081,0.0001657912,0.00004854474,0.000008649032],"category_scores_gemma":[0.00001722864,0.0001540153,0.00005921426,0.0002311847,0.0000242338,0.0001016923,0.0001353994,0.0002187612,1.018725e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007987076,"about_ca_system_score_gemma":0.000004818427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008465729,"about_ca_topic_score_gemma":0.000001348021,"domain_scores_codex":[0.9990847,0.000004713326,0.0003536636,0.0001821768,0.0001838705,0.0001908827],"domain_scores_gemma":[0.9997693,0.00004344148,0.00004585744,0.00006504104,0.00004388365,0.00003246226],"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":[0.00005567648,0.00003180943,0.00000835857,0.0004370232,0.00001312972,4.625599e-7,0.00002558082,0.01539449,0.9829856,0.0001089568,0.00002442345,0.000914462],"study_design_scores_gemma":[0.0002371131,0.00003416143,0.000008698673,0.0002311735,0.00001490443,0.000004652479,0.00003412993,0.302793,0.6963937,0.0000618687,0.00007858957,0.0001080574],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985631,0.00001864024,0.0001511285,0.00002725141,0.0009756889,0.0001567932,0.00001688603,0.00007000783,0.00002046812],"genre_scores_gemma":[0.995219,0.0000302355,0.004585166,0.0000108811,0.00009742469,0.00002109423,0.000002008867,0.00002989959,0.000004317368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2873985,"threshold_uncertainty_score":0.6280563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007201400330962758,"score_gpt":0.2107376731209049,"score_spread":0.2035362727899421,"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."}}