{"id":"W1887859263","doi":"10.1109/dftvs.1992.224357","title":"Nondeterministic adaptive routing techniques for WSI processor arrays","year":2003,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Nondeterministic algorithm; Computer science; Hypercube; Adaptive routing; Routing (electronic design automation); Distance-vector routing protocol; Parallel computing; Equal-cost multi-path routing; Network packet; Distributed computing; Static routing; Routing algorithm; Network topology; Multipath routing; Destination-Sequenced Distance Vector routing; Dynamic Source Routing; Algorithm; Computer network; Routing protocol","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":[0.0002637179,0.0004692973,0.0002866662,0.0005799339,0.0005385603,0.0006629928,0.000880955,0.0003946605,0.003101538],"category_scores_gemma":[0.0008648711,0.0002469137,0.0004089378,0.0005713524,0.0004380485,0.001269599,0.0004342319,0.0008388067,0.001062799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005286476,"about_ca_system_score_gemma":0.0003674133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009110763,"about_ca_topic_score_gemma":0.001778792,"domain_scores_codex":[0.999783,0.00003993607,0.00001696517,0.00003579374,0.0001054218,0.00001894286],"domain_scores_gemma":[0.999685,0.0001047893,0.00003789335,0.00008121329,0.00008043586,0.00001058527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006757369,0.0000515056,0.0005443348,0.0001501948,0.0000409174,0.0001554056,0.0002280166,0.2036036,0.04397166,0.2468894,0.006750516,0.4975469],"study_design_scores_gemma":[0.00001110889,0.00005256581,0.0001326976,0.00001594699,0.00001525513,0.0002151186,0.00002754132,0.9017751,0.01375663,0.05088216,0.03309024,0.00002557842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003269011,0.0001797323,0.9917079,0.0001368767,0.0000885058,0.00002662878,0.00001563818,0.0004504691,0.00412529],"genre_scores_gemma":[0.1114173,0.0007744157,0.8711622,0.0001893723,0.0001302169,0.0001876889,0.0001259766,0.0002725755,0.01574022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003101538,"threshold_uncertainty_score":0.01037562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827791653895456,"score_gpt":0.2628577428532801,"score_spread":0.2345798263143255,"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."}}