{"id":"W2162124819","doi":"10.1109/iscas.2003.1206435","title":"Fuzzy Associative Database for multiple planar object recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Fuzzy logic; Table (database); Associative property; Object (grammar); Construct (python library); Content-addressable memory; Artificial intelligence; Data mining; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Information retrieval; Mathematics; Artificial neural network; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008555052,0.0003663778,0.000895928,0.001384545,0.0005867799,0.001577717,0.001768785,0.0006607226,0.006285487],"category_scores_gemma":[0.001569796,0.0002180071,0.0007474276,0.001546211,0.0006492123,0.002720613,0.0010775,0.0006876689,0.001819326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006481493,"about_ca_system_score_gemma":0.0008500856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002353365,"about_ca_topic_score_gemma":0.002220977,"domain_scores_codex":[0.9991978,0.0001101885,0.00008841365,0.0001790809,0.0003785821,0.00004604291],"domain_scores_gemma":[0.9994117,0.0001501708,0.00003671041,0.0001856794,0.0001843726,0.00003131619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001712721,0.00008382905,0.0006745223,0.0003234854,0.00007871422,0.0002185351,0.0001705829,0.01924795,0.01401639,0.1662224,0.006711628,0.7920807],"study_design_scores_gemma":[0.00006484045,0.0002839527,0.00144884,0.0001404557,0.0001692862,0.001958986,0.0002107243,0.6264943,0.03832475,0.1931555,0.1376007,0.0001476458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005219993,0.001355386,0.987069,0.0001222348,0.00009939692,0.00008925599,0.0001560381,0.0009069518,0.004981751],"genre_scores_gemma":[0.1381673,0.001633739,0.8503169,0.0002086974,0.0001266672,0.0002809319,0.0007781725,0.00007156425,0.008415869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006285487,"threshold_uncertainty_score":0.02102703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02985541498820224,"score_gpt":0.2309619157541801,"score_spread":0.2011065007659779,"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."}}