{"id":"W4386896055","doi":"10.20944/preprints202309.1149.v1","title":"Critical Review of Neural Network Generations and Models Design","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Interpretability; Artificial intelligence; Artificial neural network; Overfitting; Machine learning; Deep learning; Recurrent neural network; Nervous system network models; Convolutional neural network; Scalability; Types of artificial neural networks","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006386588,0.0002517084,0.0004556055,0.00004699514,0.00007672668,0.000009826933,0.0002364029,0.00014192,0.00004459209],"category_scores_gemma":[0.0004737858,0.0002749171,0.0001068398,0.0001272987,0.00006066804,0.0001112699,0.0007470873,0.0006463791,0.00003990876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002737055,"about_ca_system_score_gemma":0.00002440656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002675171,"about_ca_topic_score_gemma":7.447855e-7,"domain_scores_codex":[0.9984637,0.0001551288,0.0005026311,0.0004415412,0.0001506507,0.000286379],"domain_scores_gemma":[0.9987015,0.0004770533,0.00006791894,0.0005614965,0.00009168158,0.0001004202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004592271,0.00000650486,0.0003491096,0.004763851,0.00002971632,0.000008760047,0.00005678854,0.9918562,0.001776973,0.0005348767,0.0001637247,0.0004489304],"study_design_scores_gemma":[0.000119559,0.00001512859,0.002695495,0.008699223,0.0001429947,0.00002158912,0.000008419566,0.9429319,0.01598944,0.02860564,0.0001875615,0.0005830418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3595805,0.0599615,0.5690592,0.0009282637,0.00371382,0.002203465,0.00004784393,0.002222197,0.002283185],"genre_scores_gemma":[0.9729664,0.0194414,0.006770336,0.0001633823,0.0003686406,0.0001000844,0.00001949969,0.0000747275,0.00009554753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6133859,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3530282270495351,"score_gpt":0.3795536248404555,"score_spread":0.02652539779092039,"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."}}