{"id":"W4410023076","doi":"10.1088/2634-4386/add36c","title":"NeuroMorse: a temporally structured dataset for neuromorphic computing","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Neuromorphic engineering; Computer science; Artificial intelligence; Artificial neural 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006980252,0.001393949,0.0006044985,0.002213008,0.0007285387,0.001165577,0.001908838,0.00171978,0.006208234],"category_scores_gemma":[0.005010127,0.0003024799,0.001179213,0.002188399,0.0004973884,0.001097218,0.001660868,0.0012809,0.006370967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007999128,"about_ca_system_score_gemma":0.00116047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007025627,"about_ca_topic_score_gemma":0.01667222,"domain_scores_codex":[0.9992093,0.0001417667,0.000121124,0.000191669,0.0002544771,0.00008170136],"domain_scores_gemma":[0.998437,0.0004252931,0.0001484214,0.0004057414,0.0004199397,0.0001636471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009955235,0.0006480888,0.01492529,0.003268056,0.000351294,0.0007911177,0.0002737079,0.0302902,0.01518742,0.006245321,0.8278522,0.09917184],"study_design_scores_gemma":[0.00070151,0.0009539525,0.03901741,0.0006453304,0.0001706673,0.001839089,0.000678136,0.1310488,0.03107314,0.0183609,0.775198,0.0003131349],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05270318,0.001828649,0.01509653,0.0009277895,0.0006002731,0.0004646844,0.9107218,0.01080453,0.006852462],"genre_scores_gemma":[0.04976867,0.0004375138,0.02227054,0.0002757739,0.00006806902,0.0007847681,0.9232216,0.0005617633,0.002611308],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007025627,"threshold_uncertainty_score":0.02076864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02195744381126712,"score_gpt":0.241955470675364,"score_spread":0.2199980268640969,"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."}}