{"id":"W4229004562","doi":"10.14447/jnmes.v25i1.a09","title":"Towards the Design and Analysis of Multiplexer/Demultiplexer Using Quantum Dot Cellular Automata for Nano Systems","year":2022,"lang":"en","type":"article","venue":"Journal of New Materials for Electrochemical Systems","topic":"Quantum-Dot Cellular Automata","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Demultiplexer; Multiplexer; Quantum dot cellular automaton; Multiplexing; Computer science; Energy (signal processing); Electronic engineering; Coherence (philosophical gambling strategy); Quantum dot; Physics; Topology (electrical circuits); Cellular automaton; Algorithm; Optoelectronics; Electrical engineering; Telecommunications; Quantum mechanics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004767355,0.0003334172,0.001393051,0.000433312,0.0003299768,0.0004088303,0.001785788,0.0001262548,0.000007287912],"category_scores_gemma":[0.0003363729,0.0002426895,0.0004348494,0.0008553052,0.00006211291,0.0003255627,0.0003485667,0.0001726332,3.825415e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003444377,"about_ca_system_score_gemma":0.0004603846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00019839,"about_ca_topic_score_gemma":5.864574e-7,"domain_scores_codex":[0.99555,0.0005920377,0.001789751,0.000445027,0.001024507,0.0005986656],"domain_scores_gemma":[0.995864,0.0007535909,0.00202377,0.0006742399,0.0004895981,0.0001948491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003122358,0.00008201176,0.000009519616,0.0002589178,0.0009444918,0.000005973044,0.0003331965,0.008409591,0.9848302,0.003999489,0.0007685632,0.00004578942],"study_design_scores_gemma":[0.001045613,0.000432351,0.000008058727,0.00005558452,0.0006503352,0.0002369426,0.00009322163,0.5377046,0.4579262,0.0003768433,0.001266836,0.0002034872],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4232396,0.001708855,0.5726231,0.0001205578,0.001117551,0.001093261,0.00005810524,0.00003797455,9.35626e-7],"genre_scores_gemma":[0.9892112,0.00002011123,0.01022828,0.00002527651,0.0003129556,0.0001010436,0.00002138993,0.00004419423,0.00003555616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5659716,"threshold_uncertainty_score":0.9896592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04671548995089876,"score_gpt":0.2708634162529022,"score_spread":0.2241479263020035,"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."}}