{"id":"W2405596045","doi":"10.21611/qirt.2000.032","title":"Defect Depth Estimation Using Neuro-Fuzzy System in TNDE","year":2000,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Computer vision; Fuzzy control system","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001877901,0.0001064196,0.0001452837,0.0001338451,0.00004167218,0.0000440915,0.00004078127,0.00009496938,0.00009538692],"category_scores_gemma":[0.00001268345,0.0001000187,0.00005681483,0.0003010502,0.000004115082,0.0001450492,0.00000407073,0.0001209187,0.0001961144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514535,"about_ca_system_score_gemma":0.000007902481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004249801,"about_ca_topic_score_gemma":0.0000555533,"domain_scores_codex":[0.9992781,0.00004266781,0.0002613187,0.0001263913,0.0001220025,0.000169527],"domain_scores_gemma":[0.9997624,0.00003659827,0.00001609973,0.0001348169,0.00001073122,0.00003937374],"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":[0.00001301909,0.000006398778,0.0004491704,0.00005564202,0.000008817766,0.00001400075,0.00005449713,0.9305062,0.003572072,0.0001336197,0.0005284802,0.06465811],"study_design_scores_gemma":[0.0003869948,0.00002345669,0.001136961,0.00009241339,0.000007068745,0.00006743412,0.0000531902,0.9939287,0.00200327,0.00001125877,0.002142858,0.000146363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9323918,0.00004346575,0.01522587,0.000003385997,0.0005112599,0.000223543,9.258812e-7,0.0005321977,0.05106761],"genre_scores_gemma":[0.9992978,0.000002237597,0.0004889176,0.00001994112,0.00007642919,0.000007989705,0.000001375396,0.00002331645,0.00008198434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06690606,"threshold_uncertainty_score":0.4078646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158969864283705,"score_gpt":0.2353095826836004,"score_spread":0.2137198840407633,"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."}}