{"id":"W2808118317","doi":"10.22214/ijraset.2018.4686","title":"Design and Implementation of a Low Power Vedic Multiplier","year":2018,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Multiplier (economics); Computer science; Arithmetic; Mathematics; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.001846811,0.00004715824,0.00006033736,0.001091016,0.0001644447,0.00008507769,0.0007224187,0.0000365802,0.000002164835],"category_scores_gemma":[0.0000764198,0.00004390917,0.000007030691,0.0008060687,0.0004499366,0.0002075625,0.0002656031,0.0001666193,0.000001152879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000851494,"about_ca_system_score_gemma":0.0001473557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003797086,"about_ca_topic_score_gemma":9.301264e-7,"domain_scores_codex":[0.9989923,0.000005419133,0.0001609865,0.0001916622,0.0004110707,0.0002386261],"domain_scores_gemma":[0.9992021,0.00009527802,0.00003670587,0.0001055779,0.0005059633,0.00005443083],"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":[0.00001639868,0.00005164195,0.0002823476,0.000009274594,0.00001430271,0.000004453002,0.000572593,0.0003791251,0.2625642,0.5987449,0.0001676826,0.1371931],"study_design_scores_gemma":[0.001720906,0.0005705012,0.006541944,0.00008831384,0.000001728053,0.0003148575,0.000832475,0.7431119,0.08777779,0.1533794,0.005427323,0.0002328278],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1228985,0.0000529713,0.8736174,0.002933453,0.0001730072,0.0002463626,0.000001265837,0.00002628503,0.00005083126],"genre_scores_gemma":[0.8395328,0.00004147668,0.1603205,0.00001234198,0.00003258931,0.00005502563,1.66756e-7,0.000002558458,0.000002549533],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7427328,"threshold_uncertainty_score":0.1790564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03797596724713354,"score_gpt":0.3919838952056088,"score_spread":0.3540079279584753,"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."}}