{"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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00125666,0.00019198,0.0002124197,0.0004040882,0.0006383338,0.0008608706,0.003574556,0.0001455423,0.000667103],"category_scores_gemma":[0.00004998244,0.0002038523,0.000161685,0.00104606,0.00002548711,0.0001315408,0.01115122,0.001754588,0.0003327291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655812,"about_ca_system_score_gemma":0.001749024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003680959,"about_ca_topic_score_gemma":0.00001976397,"domain_scores_codex":[0.9962182,0.0003837224,0.0002500091,0.0009704749,0.001553265,0.0006243208],"domain_scores_gemma":[0.9969132,0.0001790227,0.00007362982,0.002330047,0.0001978131,0.0003062982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006636716,0.0005113753,0.001139133,0.00076586,0.0001828661,0.0003303349,0.006315677,0.001943563,0.002543827,0.6203079,0.1872943,0.1786585],"study_design_scores_gemma":[0.0002633019,0.00008394902,0.001719742,0.000114007,0.000007981989,0.00001504063,0.0001073503,0.09383983,0.001364133,0.02869831,0.8732507,0.0005357053],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0239621,0.00552251,0.9243737,0.009012951,0.00123629,0.003337424,0.00009103998,0.00297722,0.02948675],"genre_scores_gemma":[0.4424048,0.001013445,0.5343325,0.000334033,0.002458102,0.003147866,0.0004837505,0.0001781735,0.01564743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6859563,"threshold_uncertainty_score":0.9968464,"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."}}