{"id":"W2606890761","doi":"10.1016/b978-0-12-804315-8.00016-1","title":"Applying Systems Factorial Technology to Accumulators with Varying Thresholds","year":2017,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Accumulator (cryptography); Hydraulic accumulator; Factorial; Townsend; Toolbox; Computer science; Algorithm; Mathematics; Engineering; Physics; Mathematical analysis; Mechanical engineering","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":[0.0003161975,0.0003753579,0.0003012079,0.0003572098,0.0004149527,0.001221611,0.0005255335,0.0003998798,0.0100551],"category_scores_gemma":[0.001113774,0.0002862863,0.0004067842,0.0004998613,0.000843115,0.001328689,0.0007441619,0.000710607,0.001435115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006377818,"about_ca_system_score_gemma":0.0002495787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006923368,"about_ca_topic_score_gemma":0.0010678,"domain_scores_codex":[0.9998287,0.00002947673,0.00001430615,0.00005849505,0.0000556637,0.00001338699],"domain_scores_gemma":[0.9996952,0.0001692891,0.0000257463,0.00005762646,0.00004138248,0.00001077854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002314631,0.00008179629,0.001100524,0.0002781048,0.00003988783,0.0002824604,0.000243338,0.01178947,0.3695222,0.3120887,0.003160873,0.3011811],"study_design_scores_gemma":[0.00004122071,0.0004533861,0.002967481,0.00007123909,0.00009455439,0.001541281,0.0001410027,0.2022303,0.5253699,0.1776457,0.08932941,0.0001145458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0393647,0.0008699265,0.9372773,0.0002729293,0.0002035105,0.00005222382,0.00009674473,0.002028234,0.01983438],"genre_scores_gemma":[0.4271728,0.0008356905,0.5479065,0.0001474325,0.00008566835,0.0001151994,0.0001042862,0.0004507054,0.0231818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0100551,"threshold_uncertainty_score":0.03363764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1216832226172347,"score_gpt":0.362420632133957,"score_spread":0.2407374095167223,"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."}}