{"id":"W2197062872","doi":"10.1145/2700417","title":"Efficient Fault-Tolerant Topology Reconfiguration Using a Maximum Flow Algorithm","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Control reconfiguration; Computer science; Fault tolerance; Network topology; Topology (electrical circuits); Spare part; Latency (audio); Algorithm; Logical topology; Distributed computing; Embedded system; Computer network; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007821756,0.0002899002,0.000487963,0.0008598414,0.0004626642,0.000192711,0.0006651746,0.0005489431,0.00002799719],"category_scores_gemma":[0.00004231549,0.0002645997,0.00008732597,0.0008833553,0.0001377094,0.0001964553,0.00001295531,0.00047683,0.00008027062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001635685,"about_ca_system_score_gemma":0.0001182199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002516893,"about_ca_topic_score_gemma":0.00003280575,"domain_scores_codex":[0.9977329,0.0002245616,0.0006410609,0.0006888312,0.0002215195,0.0004910771],"domain_scores_gemma":[0.9982,0.0001187855,0.0002080191,0.001019727,0.0002833151,0.0001701595],"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":[0.00006258998,0.0002895008,0.0000466687,0.00007074758,0.0002466434,0.00009019849,0.0013791,0.4451242,0.00104204,0.01938773,0.0005277251,0.5317329],"study_design_scores_gemma":[0.0007401773,0.0003488831,0.000004086732,0.0001118999,0.00001925305,0.001592078,0.001355039,0.9852102,0.001263364,0.004076661,0.004960465,0.0003178564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02513171,0.0008042861,0.9651707,0.001542027,0.0046762,0.000524952,0.00001271408,0.000582432,0.00155492],"genre_scores_gemma":[0.9828787,0.00002785538,0.01578702,0.0001194912,0.0001024495,0.0001407337,0.000003086602,0.00002236852,0.0009182459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.957747,"threshold_uncertainty_score":0.9999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03427739235105732,"score_gpt":0.2562635751955383,"score_spread":0.221986182844481,"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."}}