{"id":"W3140080171","doi":"","title":"4 - Modélisation et intégration de connaissances métier pour l'identification de défauts par règles linguistiques floues","year":2004,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Computer science; Expert system; Knowledge base; Field (mathematics); Exploit; Inference; Inference engine; Artificial intelligence; Fuzzy logic; Knowledge representation and reasoning; Node (physics); Natural language processing; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001520726,0.0002931793,0.0002650284,0.0001945962,0.0002292466,0.0003249477,0.0001220822,0.0003432502,0.0002484],"category_scores_gemma":[0.000160941,0.0003283721,0.000137723,0.000215058,0.00006984601,0.0004640411,0.00001525227,0.0003024383,0.0001042073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008424477,"about_ca_system_score_gemma":0.0002317664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001951795,"about_ca_topic_score_gemma":0.0002664548,"domain_scores_codex":[0.9978764,0.0003041196,0.0007368143,0.0003221901,0.0003433202,0.0004171496],"domain_scores_gemma":[0.9991658,0.00009231026,0.0002298788,0.0001680183,0.0002053057,0.0001387395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001132318,0.0002854505,0.001525975,0.0003620471,0.0001981739,0.00002374505,0.0109829,0.7164108,0.1927447,0.02970595,0.003503361,0.04414373],"study_design_scores_gemma":[0.003790925,0.0006397195,0.04998208,0.001757062,0.0003138624,0.00008942141,0.002030893,0.2312659,0.6535875,0.03327921,0.02204337,0.001220115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5416161,0.001552661,0.4531651,0.0009556532,0.001506064,0.0004374014,0.00003451531,0.0002358244,0.0004966693],"genre_scores_gemma":[0.993956,0.0002175696,0.002987897,0.0001104,0.002247626,0.0001018927,0.00007223617,0.00004906915,0.0002573245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4851449,"threshold_uncertainty_score":0.9999169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03029423396711655,"score_gpt":0.2830491556350415,"score_spread":0.2527549216679249,"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."}}