{"id":"W4311066705","doi":"10.36227/techrxiv.21681767.v1","title":"Generative Adversarial Networks as an Accommodative Memory for Cognitive Waveform Synthesis","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Defence Research and Development Canada","funders":"","keywords":"Computer science; Inference; Memorization; Traverse; Function (biology); Space (punctuation); Generative grammar; Algorithm; Artificial intelligence; Arithmetic; Mathematics","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":[0.0008784969,0.0007475691,0.0004638554,0.0002576527,0.0002154764,0.0007429339,0.00107849,0.0008946438,0.002402717],"category_scores_gemma":[0.002607499,0.0003376891,0.0004485105,0.0002949622,0.0009808802,0.001398463,0.001307443,0.001835727,0.0005179131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004148957,"about_ca_system_score_gemma":0.00031071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006104474,"about_ca_topic_score_gemma":0.0007489327,"domain_scores_codex":[0.9997213,0.0001010977,0.00001155279,0.00006485778,0.00006683636,0.00003431092],"domain_scores_gemma":[0.9992336,0.0004775751,0.00006851453,0.0001305687,0.00005719304,0.00003256444],"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":[0.0001442822,0.00004237966,0.0004125172,0.0000859322,0.00005343096,0.000155488,0.0001185097,0.8242619,0.01157921,0.09521471,0.001747031,0.06618465],"study_design_scores_gemma":[0.000005524495,0.00003029561,0.00004682722,0.000007315096,0.00000581294,0.0000331829,0.000006602238,0.9701014,0.002197907,0.02664199,0.0009162984,0.000006939645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01270499,0.0002364954,0.983121,0.0002622541,0.00004397501,0.0000201046,0.00004885849,0.0003863273,0.003175854],"genre_scores_gemma":[0.7703631,0.0004423183,0.2214672,0.0005070348,0.000113272,0.0001185813,0.0001694184,0.0002152474,0.006603852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002402717,"threshold_uncertainty_score":0.008037925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375647407456404,"score_gpt":0.3114225628633672,"score_spread":0.2776660887888031,"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."}}