{"id":"W4402438896","doi":"10.11159/htff24.198","title":"An Efficient Platform to Capture Flow Features In Industrial Applications","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Flow (mathematics); Mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002476741,0.000931896,0.0005418572,0.001048996,0.0003391521,0.000686314,0.001096876,0.0005878331,0.004563518],"category_scores_gemma":[0.0006513362,0.0004776439,0.0003514758,0.000644789,0.0001713497,0.001143068,0.001064756,0.0006625812,0.00185903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002686746,"about_ca_system_score_gemma":0.000631353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002645131,"about_ca_topic_score_gemma":0.00409721,"domain_scores_codex":[0.9997717,0.00001713176,0.000009250921,0.00004791035,0.0001226292,0.00003143494],"domain_scores_gemma":[0.9996979,0.00006833919,0.00002865417,0.00006878492,0.0001115466,0.00002482045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000633998,0.000436135,0.002139955,0.0002128788,0.0001054673,0.0003292713,0.0000788995,0.03942582,0.4054661,0.006109427,0.02659585,0.5184661],"study_design_scores_gemma":[0.00005822345,0.0001729595,0.001686482,0.00001577594,0.00003054395,0.0001716349,0.00002764734,0.859048,0.1228213,0.003329117,0.01259513,0.00004316475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03775694,0.000306184,0.9421168,0.00009358375,0.0001299148,0.0002015582,0.0009330024,0.01575391,0.002708171],"genre_scores_gemma":[0.3025225,0.000338501,0.684877,0.0001356249,0.00006928231,0.0004017153,0.003166698,0.0007761855,0.007712577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004563518,"threshold_uncertainty_score":0.01526648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008679791804270065,"score_gpt":0.2161888303001475,"score_spread":0.2075090384958774,"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."}}