{"id":"W2995585180","doi":"10.1109/access.2019.2951878","title":"IEEE Access Special Section: Advances in Interference Mitigation Techniques for Device-to-Device Communications","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Wireless; Key (lock); Computer network; Wireless network; Mobile broadband; Task (project management); Interference (communication); The Internet; Machine to machine; Cellular network; Telecommunications; Channel (broadcasting); Internet of Things; Embedded system; Computer security; Engineering; World Wide Web","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":[0.0001886113,0.000209182,0.000260961,0.000244969,0.00008545312,0.0002623796,0.00163346,0.000147508,0.0000528833],"category_scores_gemma":[0.00002097431,0.0002356829,0.00005271473,0.0006678579,0.00003880341,0.002224787,0.0001498156,0.0003087358,0.00003888241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000194622,"about_ca_system_score_gemma":0.0000292467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005361047,"about_ca_topic_score_gemma":0.003060735,"domain_scores_codex":[0.9988299,0.0000425956,0.0003881361,0.0002884879,0.0001410454,0.0003098266],"domain_scores_gemma":[0.9987642,0.0002375507,0.00008191219,0.0007039624,0.0001411491,0.00007124467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000395932,0.0004175217,0.1072483,0.002553977,0.0001937176,0.000009394604,0.003310601,0.3895238,0.03282243,0.002235098,0.05100283,0.4102864],"study_design_scores_gemma":[0.002237339,0.0003357981,0.03995107,0.003300426,0.00009407358,0.00002073283,0.000404694,0.1862614,0.4145553,0.004785723,0.3451339,0.002919571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.828854,0.0007856262,0.1255483,0.0005315004,0.008226879,0.003195065,0.00005845268,0.001182963,0.03161731],"genre_scores_gemma":[0.9947209,0.0004080925,0.00167574,0.0001798404,0.002253831,0.0006205252,0.00002985946,0.00005232173,0.00005884491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4073668,"threshold_uncertainty_score":0.9610869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263768216431306,"score_gpt":0.3315296491291141,"score_spread":0.2988919669648011,"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."}}