{"id":"W2073996339","doi":"10.1145/2702123.2702300","title":"How Much Faster is Fast Enough?","year":2015,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Latency (audio); Computer science; Perception; Tapping; Lag; Variety (cybernetics); Human–computer interaction; Task (project management); Artificial intelligence; Psychology; Engineering; Telecommunications; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006202232,0.00009169433,0.00008306398,0.00005034171,0.00003711292,0.0002470723,0.0004923993,0.00003003138,0.0001038548],"category_scores_gemma":[0.00001748906,0.00007085711,0.00004987002,0.0001282196,0.00001746595,0.001069347,0.0001905648,0.0000718408,0.001009738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002825894,"about_ca_system_score_gemma":0.00003606522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002055567,"about_ca_topic_score_gemma":0.000002484201,"domain_scores_codex":[0.9992825,0.00002213086,0.00006459972,0.0002363629,0.0001869191,0.0002074473],"domain_scores_gemma":[0.999325,0.00001682708,0.00002948637,0.0003226629,0.0002004668,0.000105525],"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":[0.00001817351,0.0001559282,0.0007976957,0.000007343076,0.00006394938,0.00004395353,0.01381995,0.000001415569,0.03354831,0.1742432,0.7602579,0.01704216],"study_design_scores_gemma":[0.0009472962,0.0003926613,0.001862899,0.00001553037,0.000008153173,0.00005785884,0.006459559,0.01041829,0.4700613,0.003081272,0.506143,0.000552156],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009552024,0.00003343182,0.7116418,0.01746735,0.0007023642,0.00007984395,0.00000233042,0.00003147795,0.2604893],"genre_scores_gemma":[0.9484698,0.000001056733,0.004699202,0.005747169,0.00007846464,0.000003797992,0.00000140485,0.000004892066,0.04099419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9389178,"threshold_uncertainty_score":0.9997681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349495674168694,"score_gpt":0.258346595296869,"score_spread":0.2233970278799996,"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."}}