{"id":"W6968863970","doi":"10.5281/zenodo.4058838","title":"Silent Noise: Channel Noise in Visual Communications","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Visual communication; Noise (video); Communication design; Graphics; Channel (broadcasting); Fidelity; Communications system; Terminology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00037796,0.0001088475,0.0001231393,0.0001805752,0.0008473431,0.0008386403,0.002950192,0.00003979339,0.0009906637],"category_scores_gemma":[0.0004488474,0.0001211144,0.00003604727,0.001244739,0.00009513932,0.0005076313,0.003326486,0.0002172677,0.005144174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007519326,"about_ca_system_score_gemma":0.00000632523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001060365,"about_ca_topic_score_gemma":4.976571e-7,"domain_scores_codex":[0.9985116,0.0002908814,0.0002674438,0.0003636449,0.0003061728,0.0002602917],"domain_scores_gemma":[0.9986855,0.0000228581,0.00008164064,0.0007129206,0.0002737208,0.0002233354],"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":[0.00006887934,0.001638522,0.00005514413,0.0002148026,0.00008713947,0.00006534145,0.02126672,0.00177682,0.01015088,0.2849359,0.460537,0.2192028],"study_design_scores_gemma":[0.0003923926,0.0001185584,0.0003546643,0.000017163,0.000003496839,0.00001076007,0.0002236416,0.2320253,0.0002013928,0.0001427396,0.766356,0.0001539018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003670212,0.0002266836,0.8644061,0.0353566,0.0001716833,0.0008537368,0.0002616003,0.002651378,0.092402],"genre_scores_gemma":[0.9935341,0.0002287994,0.001717157,0.0023795,0.00008858177,6.300273e-8,0.001342268,0.000469168,0.000240322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9898639,"threshold_uncertainty_score":0.9999226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07334605655952856,"score_gpt":0.3039716357720551,"score_spread":0.2306255792125265,"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."}}