{"id":"W2105003732","doi":"10.1109/wescan.1995.494068","title":"An algebraic construction method for cellular neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Cellular neural network; Very-large-scale integration; Computer science; Asynchronous communication; Artificial neural network; Interconnection; Feature (linguistics); Algebraic number; Process (computing); Component (thermodynamics); Image processing; Signal processing; Artificial intelligence; Image (mathematics); Theoretical computer science; Embedded system; Mathematics; Computer hardware; Computer network; Digital signal processing","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":[],"consensus_categories":[],"category_scores_codex":[0.0002335056,0.000107136,0.0001186062,0.00003848259,0.000181581,0.0001624731,0.0003714876,0.00007459061,0.0001246957],"category_scores_gemma":[0.00001415713,0.00009665352,0.00006077199,0.0002910843,0.00003424617,0.000747935,0.00004029138,0.00008524981,0.000005516969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002136866,"about_ca_system_score_gemma":0.000004269928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007874072,"about_ca_topic_score_gemma":0.00001037783,"domain_scores_codex":[0.9989411,0.0001311706,0.0001933032,0.0003707921,0.0001099411,0.0002537347],"domain_scores_gemma":[0.9992382,0.0001315603,0.00005441087,0.0004145703,0.00007230118,0.00008898358],"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":[0.000006537427,0.00009786823,0.0004052554,0.00001373405,0.00001044456,0.000002635675,0.0002083685,0.1200967,0.001243115,0.22718,0.00120239,0.6495329],"study_design_scores_gemma":[0.0001900916,0.0001241333,0.00003878451,0.000001377051,0.000004182962,0.00001424861,0.000009310628,0.9923989,0.001199081,0.005264588,0.000630362,0.0001250074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003333472,0.00009278677,0.9943616,0.0008000765,0.0006244603,0.0002534504,5.896741e-7,0.0002603638,0.0002731422],"genre_scores_gemma":[0.5838617,0.000005458325,0.4153379,0.0004660055,0.0002085333,0.00001474086,0.000006294287,0.000006723276,0.00009266986],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8723021,"threshold_uncertainty_score":0.3941416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921907580554866,"score_gpt":0.2487268291691858,"score_spread":0.2295077533636372,"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."}}