{"id":"W2014772392","doi":"10.1007/s11042-010-0703-z","title":"Improving online gaming experience using location awareness and interaction details","year":2011,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Quality of experience; Latency (audio); Human–computer interaction; Set (abstract data type); Orientation (vector space); Constraint (computer-aided design); Computer network; Quality of service; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000205391,0.0004780065,0.0004163288,0.0005144133,0.0001936178,0.0008355444,0.0004796713,0.0004228405,0.005118797],"category_scores_gemma":[0.001395159,0.0001573675,0.0002520158,0.0003148513,0.00008818099,0.001013082,0.0006845849,0.0003591526,0.0005420548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009635383,"about_ca_system_score_gemma":0.0001631439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008352085,"about_ca_topic_score_gemma":0.001693915,"domain_scores_codex":[0.9997689,0.00005835349,0.00001446778,0.000039249,0.00008255911,0.00003643146],"domain_scores_gemma":[0.9992995,0.0003342001,0.00006586284,0.00007999824,0.0001406322,0.00007969799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001893167,0.002970229,0.01905024,0.0005974408,0.0001625019,0.000346763,0.0006620717,0.007503454,0.3151929,0.001106187,0.002135079,0.6483799],"study_design_scores_gemma":[0.0004282004,0.007184817,0.3364823,0.0002026218,0.001802107,0.002166417,0.002536035,0.2858629,0.3377395,0.004105602,0.02120991,0.0002795307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8822343,0.000610831,0.1014429,0.0001568145,0.00007189983,0.0001572758,0.0001750017,0.001894311,0.01325675],"genre_scores_gemma":[0.9719815,0.0002166073,0.02469179,0.00003583417,0.00002342803,0.00004102954,0.00009417874,0.00005972098,0.002855972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005118797,"threshold_uncertainty_score":0.01712406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09000140225195971,"score_gpt":0.3105439723065407,"score_spread":0.220542570054581,"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."}}