{"id":"W3107346677","doi":"10.1021/acsami.0c10796","title":"From Memristive Materials to Neural Networks","year":2020,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada","keywords":"Memristor; Neuromorphic engineering; Computer science; Artificial neural network; Resistive random-access memory; Process (computing); Computer architecture; Artificial intelligence; Electronic engineering; Electrical engineering; Engineering; Voltage","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007793088,0.0003134152,0.0004539631,0.0000274358,0.00006925276,0.0001630378,0.0003320027,0.00009541961,0.0004946681],"category_scores_gemma":[0.00002066949,0.000300628,0.0000115578,0.0001085089,0.00002611328,0.0001178063,0.0002218527,0.0001195071,0.000260214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000025122,"about_ca_system_score_gemma":0.000002782544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000135383,"about_ca_topic_score_gemma":0.000001286017,"domain_scores_codex":[0.9986849,0.0000299998,0.0004201921,0.0003794052,0.000109185,0.0003763389],"domain_scores_gemma":[0.9995126,0.00007607933,0.00006702268,0.0001930935,0.00001834311,0.0001328823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001630903,0.0000036985,5.770596e-7,0.00002915195,0.00003054311,0.000007393677,0.0006280193,0.07347338,0.9235258,0.00005802401,0.000880308,0.001200005],"study_design_scores_gemma":[0.0001977094,0.0000501818,0.0000204082,0.00002600367,0.00001843329,0.000001369536,0.0001645644,0.0002063586,0.9985734,0.0001771015,0.0002400036,0.0003245119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939308,0.00008103203,0.002947953,0.0001959729,0.001422075,0.0003411836,0.0001288461,0.0006654789,0.0002866996],"genre_scores_gemma":[0.9971182,0.00001420712,0.0005441596,0.0008493396,0.001299688,0.00004758447,0.00004523477,0.00007183709,0.000009709996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07504755,"threshold_uncertainty_score":0.9999446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01640323548980665,"score_gpt":0.2262435731159542,"score_spread":0.2098403376261475,"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."}}