{"id":"W4225849263","doi":"10.1109/access.2022.3158753","title":"Mapping Applications Intents to Programmable NDN Data-Planes via Event-B Machines","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Semantics (computer science); Event (particle physics); Forwarding plane; Computer network; Distributed computing; Content delivery; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001284911,0.0006440904,0.000371432,0.0004795166,0.0004474188,0.002405785,0.001489862,0.000887758,0.002228988],"category_scores_gemma":[0.003812339,0.0004647312,0.00101399,0.0003309683,0.001218891,0.002854829,0.00153256,0.001590647,0.0006956052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001025655,"about_ca_system_score_gemma":0.001060712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003183196,"about_ca_topic_score_gemma":0.002513554,"domain_scores_codex":[0.9988995,0.000353941,0.0001027624,0.0002069663,0.000338968,0.00009787887],"domain_scores_gemma":[0.9987198,0.0005695297,0.0001412113,0.0003592928,0.0001474253,0.00006277802],"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":[0.0002495472,0.0002124611,0.003210432,0.0002349961,0.00004725676,0.0008750907,0.001408262,0.3572327,0.02528814,0.5282418,0.004296053,0.0787033],"study_design_scores_gemma":[0.00002824056,0.00004265617,0.0002099226,0.00003159672,0.00001603562,0.0001024202,0.0001123405,0.8818139,0.01191434,0.08896227,0.01674538,0.00002090169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01246513,0.00003787225,0.9815638,0.0001513904,0.00003203796,0.0001273666,0.0001084517,0.00225321,0.003260806],"genre_scores_gemma":[0.433306,0.0002394488,0.5602687,0.0001790402,0.00004130874,0.000447432,0.0004943474,0.0003846014,0.004639174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003183196,"threshold_uncertainty_score":0.00745666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06508792739659432,"score_gpt":0.3230119035860743,"score_spread":0.25792397618948,"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."}}