{"id":"W3013782022","doi":"10.18280/ts.370111","title":"A Deep Learning Model for Striae Identification in End Images of Float Glass","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Float glass; Convolutional neural network; Artificial intelligence; Float (project management); Computer science; Process (computing); Artificial neural network; Deep learning; Pattern recognition (psychology); Computer vision; Materials science; Engineering; Composite material","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":[0.0001921544,0.0004284124,0.0003443478,0.000417533,0.0001878372,0.00043717,0.0007723505,0.0007252365,0.0009974218],"category_scores_gemma":[0.0003440826,0.0002916583,0.0005891057,0.0002709306,0.0002557551,0.0005638056,0.0003775474,0.000740417,0.0003599171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007092174,"about_ca_system_score_gemma":0.0006478741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226498,"about_ca_topic_score_gemma":0.01426139,"domain_scores_codex":[0.999916,0.000005793858,0.000004158634,0.00003265503,0.00002136294,0.00002010567],"domain_scores_gemma":[0.9999226,0.00001723121,0.00001195707,0.000007126057,0.00003387635,0.000007257007],"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":[0.0002733678,0.0001270561,0.003633767,0.0001014939,0.00007962304,0.0002291044,0.00009296049,0.6338001,0.06902351,0.003375383,0.002329916,0.2869336],"study_design_scores_gemma":[0.000001352704,0.00001343275,0.0003783575,0.000002972538,0.000005356209,0.00001420498,0.000003057615,0.9965315,0.002563486,0.0002629104,0.0002202563,0.000003125006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1055088,0.0006789778,0.8892401,0.0002501653,0.00007619358,0.00005423853,0.000234292,0.00133872,0.002618542],"genre_scores_gemma":[0.8521928,0.0005303049,0.135332,0.0002265643,0.00003720888,0.00008196347,0.0005733993,0.00007967055,0.010946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01226498,"threshold_uncertainty_score":0.02438724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158263414958313,"score_gpt":0.23672378451552,"score_spread":0.2051411503659369,"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."}}