{"id":"W3139549852","doi":"10.1145/3448104","title":"SplitSR","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Residual; Mobile device; Inference; Latency (audio); Deep learning; Computer engineering; Cloud computing; Bilinear interpolation; Convolutional neural network; Artificial intelligence; Ranging; Computation; Computer vision; Algorithm; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0001422755,0.0001981937,0.0002604261,0.0001343585,0.0001853134,0.0001812827,0.002966885,0.0001165404,0.000002920637],"category_scores_gemma":[0.002273278,0.0001416982,0.00007577051,0.0006379278,0.0002669652,0.0008908999,0.004568218,0.000431323,0.000004095198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000565457,"about_ca_system_score_gemma":0.00004188822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003944712,"about_ca_topic_score_gemma":5.469167e-7,"domain_scores_codex":[0.9987246,0.000007282109,0.0002340005,0.0005266789,0.0002311799,0.0002763066],"domain_scores_gemma":[0.9982002,0.0001529572,0.0002654509,0.0008708638,0.0004889093,0.00002156537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003970192,0.0002182553,0.00116481,0.0001602415,0.00004954734,0.000006993096,0.0005261974,0.000003103653,0.7349803,0.03540719,0.00266932,0.2247743],"study_design_scores_gemma":[0.00008611124,0.0001783273,0.0001632146,0.0003192069,0.000006069788,0.00005604542,0.0009787463,0.0002390455,0.7873461,0.2084018,0.002093084,0.0001322937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9465475,0.005156104,0.01111797,0.01414643,0.0005339785,0.0009773049,0.000008341008,0.004852855,0.0166595],"genre_scores_gemma":[0.8604812,0.0004909586,0.1382292,0.0001609354,0.00001374411,0.0001608405,1.140539e-7,0.00001493958,0.0004480339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2246421,"threshold_uncertainty_score":0.5778285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049224078120243,"score_gpt":0.2649735983193623,"score_spread":0.2544813575381599,"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."}}