{"id":"W4246733687","doi":"10.32920/ryerson.14652267","title":"IC testing using thermal image based on intelligent classification methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Support vector machine; Feature extraction; Computer science; Adaptive neuro fuzzy inference system; Histogram; Perceptron; Fuzzy logic; Segmentation; Artificial neural network; Image (mathematics); Fuzzy control system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009735831,0.0002984449,0.0003521105,0.0002317237,0.00008352706,0.0002637896,0.0001301903,0.000494241,0.0001931725],"category_scores_gemma":[0.0002959731,0.0002795837,0.0001749177,0.0002890466,0.00001373731,0.00006075305,0.00008547357,0.0008207842,0.00002493217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003491244,"about_ca_system_score_gemma":0.00009767543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001573845,"about_ca_topic_score_gemma":0.000003131973,"domain_scores_codex":[0.9983244,0.0003135925,0.0005122296,0.0003936754,0.0002341597,0.0002219524],"domain_scores_gemma":[0.9987807,0.0003116312,0.0001231138,0.0005444894,0.0001695421,0.00007052505],"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":[0.000007552112,0.00002012106,0.00003364151,0.000175661,0.000034343,0.000006231876,0.00007195232,0.533316,0.3582054,0.00001245422,0.00004875521,0.1080679],"study_design_scores_gemma":[0.00008860423,0.00001990066,0.0002594065,0.0003004609,0.00002734272,0.00000322124,0.0001630973,0.8605899,0.1380583,0.00001050835,0.000221382,0.0002578086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.113432,0.00006679198,0.8646415,0.00001132945,0.002870255,0.000370855,0.000003991995,0.0005437397,0.01805956],"genre_scores_gemma":[0.8366796,0.00000237911,0.1626126,0.0000317255,0.0004926712,0.00003681449,0.00002255257,0.00007185822,0.00004982476],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7232476,"threshold_uncertainty_score":0.9999656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1928518437463194,"score_gpt":0.3724424168196926,"score_spread":0.1795905730733732,"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."}}