{"id":"W4411361306","doi":"10.1016/j.compeleceng.2025.110536","title":"A neural cantonese speech converter using QCA for nanocomputing","year":2025,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Quantum-Dot Cellular Automata","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Speech recognition; Computer science","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":[0.0002096967,0.0001910323,0.000365432,0.0002062617,0.000781059,0.0008712437,0.0008409046,0.0005968898,0.007638674],"category_scores_gemma":[0.0004283056,0.0001167716,0.0002092894,0.0003197149,0.0003736702,0.0005818731,0.0003435894,0.0005706507,0.001389203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007411449,"about_ca_system_score_gemma":0.000671353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004577691,"about_ca_topic_score_gemma":0.01128818,"domain_scores_codex":[0.9998307,0.00002029877,0.000007114484,0.00004568251,0.00007460955,0.00002148322],"domain_scores_gemma":[0.9997745,0.00005807451,0.000008280579,0.00004088243,0.00008484101,0.0000334999],"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":[0.0006401862,0.0003956354,0.00104478,0.000407592,0.00008425822,0.0006194001,0.0002576127,0.01629455,0.6969296,0.1121133,0.01359754,0.1576156],"study_design_scores_gemma":[0.00009388771,0.0003110642,0.0008566034,0.00005334216,0.00006945627,0.0003311425,0.0000811673,0.6677291,0.2790296,0.0145389,0.03681134,0.00009427486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3923471,0.001995709,0.4833279,0.003253867,0.00198536,0.0003608296,0.0009521111,0.005968751,0.1098084],"genre_scores_gemma":[0.8921755,0.0002494481,0.08603532,0.0003032041,0.00006255126,0.00006167294,0.0001685283,0.0001134969,0.02083031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007638674,"threshold_uncertainty_score":0.02555388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057164107198611,"score_gpt":0.2387872758878834,"score_spread":0.2282156348158973,"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."}}