{"id":"W4387969948","doi":"10.1109/mcom.2023.10298086","title":"Global Communications Newsletter","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Telecommunications; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002551184,0.000136528,0.0001473489,0.0001306058,0.0003697577,0.0003265511,0.009155873,0.00005919798,0.00001434279],"category_scores_gemma":[0.00004844144,0.0001445092,0.00007612475,0.001691701,0.0002622972,0.001074618,0.003468879,0.0001571952,0.008178222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006734862,"about_ca_system_score_gemma":0.00008099792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003101988,"about_ca_topic_score_gemma":0.0002099476,"domain_scores_codex":[0.9988866,0.0001092384,0.0003169071,0.0002635757,0.0001324886,0.0002912347],"domain_scores_gemma":[0.9890006,0.0002191518,0.00009235422,0.0104675,0.0001013738,0.0001190651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001517992,0.0001771733,0.001842994,0.00000743206,0.00003300639,0.000003756462,0.0001361459,0.00003950114,0.0001270816,0.3990351,0.513728,0.08486829],"study_design_scores_gemma":[0.000230014,0.0000169614,0.009882664,0.00001372651,0.000006182844,0.00001810713,0.00001171429,0.03667576,0.00001406513,0.01594746,0.9369739,0.0002094752],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001884863,0.001186255,0.1997031,0.1905317,0.001094471,0.0005947098,0.0002952043,0.003066231,0.6016434],"genre_scores_gemma":[0.8255718,0.001787361,0.159647,0.00783625,0.000138987,0.000297992,0.0008186686,0.00003040738,0.00387147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.823687,"threshold_uncertainty_score":0.9962051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08188808701700448,"score_gpt":0.315273080390297,"score_spread":0.2333849933732925,"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."}}