{"id":"W3206457735","doi":"10.3390/bdcc5040056","title":"6G Cognitive Information Theory: A Mailbox Perspective","year":2021,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Distributed computing; Transmission (telecommunications); Artificial intelligence; Telecommunications","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":[0.0008042926,0.000246882,0.0002621564,0.0001519617,0.0005702555,0.000741736,0.0006503065,0.00008144064,0.00000377651],"category_scores_gemma":[0.001403104,0.0002509174,0.00004944596,0.000649526,0.0001209387,0.001343865,0.003165231,0.0003005467,0.0001040762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003388789,"about_ca_system_score_gemma":0.0002527036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002405038,"about_ca_topic_score_gemma":0.000003016949,"domain_scores_codex":[0.997926,0.0002840309,0.0003254265,0.0007104679,0.000292374,0.0004617046],"domain_scores_gemma":[0.9972637,0.001069008,0.0001902046,0.0004570209,0.0008807509,0.0001392904],"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":[0.00002100952,0.00004533945,0.0006364876,0.00002413404,0.00009219024,0.00006013176,0.00504646,4.081665e-7,0.00002038954,0.01092335,0.0005900853,0.98254],"study_design_scores_gemma":[0.01324467,0.000782688,0.1116459,0.00494549,0.0008459622,0.002395173,0.08516154,0.647405,0.007299804,0.07235519,0.04873451,0.005184123],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03591511,0.0009719177,0.9437987,0.0003301386,0.002833214,0.0001943286,0.00003368976,0.0001936566,0.01572927],"genre_scores_gemma":[0.9923181,0.00003108979,0.00412473,0.001614085,0.001521051,0.000002901327,0.0003244197,0.00001166283,0.00005196905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9773559,"threshold_uncertainty_score":0.9999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05865866255885519,"score_gpt":0.291988622259503,"score_spread":0.2333299597006478,"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."}}