{"id":"W4393441092","doi":"10.1007/s12243-024-01026-4","title":"ICIN 2023 special issue — Emergence of the data and intelligence networking across the edge-cloud continuum","year":2024,"lang":"en","type":"article","venue":"Annals of Telecommunications","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Cloud computing; Enhanced Data Rates for GSM Evolution; Computer science; Physics; Data science; 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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.008176339,0.00008618245,0.0001622986,0.00006605655,0.0005056049,0.0003445284,0.007190093,0.00002700979,0.0003502169],"category_scores_gemma":[0.002155066,0.00004804483,0.00007363655,0.00171316,0.000595442,0.000226689,0.005947498,0.0002003259,0.00008061513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002307485,"about_ca_system_score_gemma":0.00004556083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001627763,"about_ca_topic_score_gemma":0.00129133,"domain_scores_codex":[0.9978236,0.0003164922,0.0007098463,0.0004000263,0.0005503729,0.0001996772],"domain_scores_gemma":[0.9915726,0.002447574,0.0002571373,0.005428483,0.0002590521,0.00003515422],"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":[0.000001703409,0.00002375902,0.0006161184,0.000005412727,0.00002135268,1.438183e-7,0.001472584,0.0001135351,0.00001787408,0.001636403,0.7265428,0.2695483],"study_design_scores_gemma":[0.00001481748,0.00001038707,0.005743577,0.00008856221,0.000012132,0.00000139922,0.003551533,0.0229846,0.0002963252,0.009315779,0.9579163,0.00006461405],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2138645,0.08020899,0.07592674,0.4231316,0.04524769,0.003160274,0.003439037,0.0002774551,0.1547436],"genre_scores_gemma":[0.9891078,0.002264364,0.0009241105,0.0003460393,0.001047236,0.000005998102,0.00002793182,0.000008156587,0.00626839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7752432,"threshold_uncertainty_score":0.9981815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3709295336196575,"score_gpt":0.4944695940746489,"score_spread":0.1235400604549914,"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."}}