{"id":"W1540530916","doi":"10.1007/978-3-642-01044-6_69","title":"Nonnegative Matrix Factorization for Binary Data to Extract Elementary Failure Maps from Wafer Test Images","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Non-negative matrix factorization; Binary number; Logical matrix; Probabilistic logic; Wafer; Computer science; Matrix (chemical analysis); Superposition principle; Binary data; Matrix decomposition; Pattern recognition (psychology); Algorithm; Artificial intelligence; Mathematics; Engineering; Arithmetic; Materials science","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":[0.0005354164,0.000491161,0.0008149052,0.0008877342,0.0003017442,0.000144719,0.0005957188,0.000402879,0.0000877033],"category_scores_gemma":[0.0008189276,0.0004883698,0.00005282278,0.001185395,0.00006550572,0.000670072,0.0003836784,0.0002650284,0.00005017098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003373458,"about_ca_system_score_gemma":0.00008030934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006236236,"about_ca_topic_score_gemma":0.002468892,"domain_scores_codex":[0.9973311,0.00005400553,0.0009949072,0.001135395,0.0002550318,0.0002296025],"domain_scores_gemma":[0.9966912,0.0004613977,0.0003166874,0.001662546,0.0007797803,0.00008839922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001257647,0.0004259633,0.02328695,0.001343643,0.01248235,0.00001199195,0.006447724,0.001148064,0.01180871,0.003063547,0.8356167,0.1042386],"study_design_scores_gemma":[0.002057945,0.000286188,0.02798352,0.001087724,0.00878065,0.000004171436,0.003459005,0.03397983,0.0009950817,0.002316324,0.9162877,0.002761881],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.001599549,0.03486335,0.791612,0.00162927,0.00631198,0.009799698,0.138028,0.001725687,0.01443055],"genre_scores_gemma":[0.4581169,0.02581525,0.01024104,0.00007388401,0.005559356,0.0001233266,0.464908,0.0005026315,0.03465957],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7813709,"threshold_uncertainty_score":0.9997568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1126550926836746,"score_gpt":0.3503634868738844,"score_spread":0.2377083941902098,"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."}}