{"id":"W4311881968","doi":"10.21203/rs.3.rs-2348688/v1","title":"A New Self-Shrinking Generator","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Mitacs","keywords":"Self-shrinking generator; Generator (circuit theory); NIST; Randomness; Computer science; Resizing; Generalization; Mathematics; Electrical engineering; Physics; Engineering; Induction generator; Mathematical analysis; Power (physics); Speech recognition; Voltage; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002529809,0.0003840282,0.000473196,0.0006639738,0.0005616621,0.0006759385,0.0007928106,0.0007939485,0.01056767],"category_scores_gemma":[0.001323748,0.0002677099,0.0004789191,0.0003756808,0.0006221685,0.0009462098,0.001115824,0.0007901449,0.001944787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002549359,"about_ca_system_score_gemma":0.0003050615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001352961,"about_ca_topic_score_gemma":0.0001621367,"domain_scores_codex":[0.9998062,0.00005155619,0.000009245199,0.0000621463,0.00004634288,0.00002449312],"domain_scores_gemma":[0.999463,0.000201123,0.00003740861,0.0001239281,0.00009551085,0.0000789114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001416546,0.00008430479,0.0004307989,0.0001838452,0.00002129377,0.0004028563,0.0002849674,0.01786887,0.06570066,0.7920157,0.009679171,0.1131858],"study_design_scores_gemma":[0.0001425801,0.0002281213,0.0004718527,0.00003850145,0.00003737409,0.001083692,0.00007238988,0.3390793,0.02770455,0.580003,0.05107911,0.00005974025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1431246,0.0008594368,0.7730641,0.001206433,0.001496803,0.0001988651,0.000350584,0.001617801,0.07808133],"genre_scores_gemma":[0.7270288,0.0007220031,0.1966772,0.0006684649,0.0007581526,0.0004017384,0.0004359029,0.0008221515,0.07248552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01056767,"threshold_uncertainty_score":0.03535235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06771155426041596,"score_gpt":0.3849727865038159,"score_spread":0.3172612322434,"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."}}