{"id":"W2162113562","doi":"10.1109/mwscas.1989.101899","title":"VLSI design of optically coupled neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Resistor; Massively parallel; Artificial neural network; Computer science; Very-large-scale integration; Electronic circuit; Biological neural network; CMOS; Construct (python library); Operational amplifier; Electronic engineering; Amplifier; Artificial intelligence; Voltage; Electrical engineering; Engineering; Embedded system; Parallel computing; Machine learning; Computer network","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":[0.00007818111,0.00007664138,0.0001064891,0.00001629846,0.00002130495,0.000006261117,0.00005397388,0.00003174588,0.0000635453],"category_scores_gemma":[0.00003067263,0.00006863711,0.00002526504,0.00009215156,0.00001475305,0.00007092131,0.000006914358,0.00009433471,0.000003232526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006729366,"about_ca_system_score_gemma":0.000002516205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.720144e-7,"about_ca_topic_score_gemma":1.455102e-7,"domain_scores_codex":[0.999562,0.00001733225,0.0001371125,0.00007151464,0.00004877002,0.0001632368],"domain_scores_gemma":[0.9997144,0.0001220105,0.00001148225,0.00009347744,0.0000161542,0.0000424827],"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":[0.000003561573,0.000003921011,0.0000186095,0.000006944296,0.000004746906,0.000003745076,0.000007851576,0.9819803,0.01626192,0.0008842613,0.00002146317,0.0008027077],"study_design_scores_gemma":[0.0001348142,0.00002929313,0.00004026117,0.000004650097,0.000003974756,0.000008385156,0.00001159211,0.9724975,0.02707826,0.00008331254,0.00002747794,0.00008050183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1369006,0.0000916718,0.8604784,0.000003271926,0.0001565012,0.00006817962,5.381721e-8,0.0001534457,0.002147915],"genre_scores_gemma":[0.9804943,0.00001017655,0.01937538,0.00003017157,0.00002339658,0.000001500161,3.044297e-7,0.00001325206,0.00005156114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8435937,"threshold_uncertainty_score":0.279894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01992599543489178,"score_gpt":0.2203305592652253,"score_spread":0.2004045638303336,"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."}}