{"id":"W7084135053","doi":"10.1109/infocomwkshps65812.2025","title":"IEEE INFOCOM 2025 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","year":2025,"lang":"en","type":"paratext","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Communications system; Bandwidth (computing); Focus (optics); Data compression; Transmission (telecommunications); Channel (broadcasting); Entropy (arrow of time); Image compression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003440443,0.001076031,0.001185563,0.0007226597,0.0006645768,0.002047984,0.01212349,0.0009291189,0.00194646],"category_scores_gemma":[0.00002525875,0.0009673097,0.0003625803,0.00158477,0.0003750531,0.0007719251,0.002029536,0.002109909,0.01864962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002008387,"about_ca_system_score_gemma":0.001899228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001584049,"about_ca_topic_score_gemma":0.00008580332,"domain_scores_codex":[0.9951968,0.000281416,0.00122702,0.001513916,0.0007795811,0.001001256],"domain_scores_gemma":[0.9912691,0.0007641659,0.0006278459,0.006294986,0.0006846723,0.0003592884],"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":[0.00000942956,0.0002002517,0.000005192627,0.0002212325,0.0001076599,0.000008204978,0.0002031005,0.00030312,0.00000757729,0.1384554,0.801715,0.05876377],"study_design_scores_gemma":[0.0009206915,0.0003319357,0.0001089952,0.002909817,0.00006907378,0.00002116716,0.00003444398,0.1287984,0.0004450173,0.006831313,0.8573914,0.002137682],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.000003357346,0.0002858349,0.5776912,0.004902892,0.003866468,0.0005696314,0.0000662014,0.0002518311,0.4123626],"genre_scores_gemma":[0.01544276,0.004954029,0.1941464,0.01254729,0.0009930027,0.0005130367,0.0003899274,0.00008477807,0.7709288],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3835448,"threshold_uncertainty_score":0.9992777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04551263992975755,"score_gpt":0.3086906982136493,"score_spread":0.2631780582838917,"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."}}