{"id":"W4402753594","doi":"10.1109/mwscas60917.2024.10658870","title":"Memristive-Based Full Adder","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Adder; Computer science; Computer architecture; Telecommunications","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":[0.00008491535,0.0002861374,0.0002044747,0.0004064826,0.0003596195,0.0005621125,0.0009722294,0.000367216,0.004812445],"category_scores_gemma":[0.0001713904,0.0001676029,0.0002886174,0.0002514355,0.0001333949,0.000806982,0.0002747888,0.0002513332,0.0008821943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003171766,"about_ca_system_score_gemma":0.000475774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005453962,"about_ca_topic_score_gemma":0.001510393,"domain_scores_codex":[0.9999198,0.00000617481,0.000009141849,0.00001658614,0.00003625466,0.00001209824],"domain_scores_gemma":[0.9999281,0.000009194428,0.00001031175,0.00001277378,0.00003395393,0.000005604871],"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":[0.0003764895,0.0001408752,0.0004997575,0.001017991,0.000186209,0.0008026164,0.0001341607,0.03320863,0.4895647,0.06020761,0.009676295,0.4041846],"study_design_scores_gemma":[0.0001095013,0.0008595291,0.001394266,0.0001635459,0.0002883533,0.002696342,0.00007363123,0.3985035,0.4589083,0.01969226,0.1171849,0.000125876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1975504,0.007272541,0.7145545,0.001168189,0.001101029,0.0003272484,0.001220169,0.006974421,0.06983156],"genre_scores_gemma":[0.7457553,0.00176236,0.2320363,0.0006223039,0.00009029732,0.000132938,0.0003746859,0.00007751972,0.01914832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004812445,"threshold_uncertainty_score":0.01609927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01139724450202675,"score_gpt":0.2304122629430652,"score_spread":0.2190150184410385,"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."}}