{"id":"W4416883163","doi":"10.37665/cltrnnc93944","title":"Rinsing Study Based on Inline Cleaning Processes","year":2018,"lang":"","type":"article","venue":"Cleaning and Coating Conference","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003239071,0.0003903772,0.000507932,0.0002972318,0.000643355,0.0004855118,0.0004642447,0.0006748655,0.006064266],"category_scores_gemma":[0.0006942916,0.0002602713,0.0004003955,0.0003478515,0.0002588515,0.000641679,0.0002323397,0.0003938619,0.0005967321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003158609,"about_ca_system_score_gemma":0.0002828247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001590128,"about_ca_topic_score_gemma":0.001985718,"domain_scores_codex":[0.9996519,0.00002861025,0.00002125752,0.00008934282,0.0001227358,0.00008613362],"domain_scores_gemma":[0.9990665,0.0002273365,0.0001239233,0.0001336075,0.0004003255,0.0000482893],"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":[0.0003860099,0.00009141504,0.001012184,0.0001780847,0.00001892581,0.0001578,0.0001344202,0.0002360763,0.992426,0.0001262658,0.0002671151,0.004965599],"study_design_scores_gemma":[0.00002269779,0.00126419,0.008250304,0.00001258988,0.000083033,0.0001139812,0.0002635318,0.002501238,0.9838004,0.00005616365,0.003615192,0.00001663516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941917,0.001133517,0.00213652,0.00004984974,0.00007536438,0.00003804783,0.0001616529,0.00008828164,0.002125008],"genre_scores_gemma":[0.9903494,0.0008037496,0.002595849,0.00008907318,0.00002887467,0.00002861321,0.0002815316,0.0000684279,0.005754503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006064266,"threshold_uncertainty_score":0.02028698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.045747158659973,"score_gpt":0.2771505718126047,"score_spread":0.2314034131526317,"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."}}