{"id":"W3164998268","doi":"10.31234/osf.io/fzwgs","title":"Predicting group benefits in joint multiple object tracking","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Task (project management); Joint (building); Variance (accounting); Group (periodic table); Computer science; Cognitive psychology; Psychology; Engineering; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003644113,0.0006970211,0.0007498286,0.001514093,0.0006050377,0.00130209,0.0005032987,0.001235092,0.002248315],"category_scores_gemma":[0.008104138,0.0002308415,0.000646013,0.001234144,0.0003734599,0.001327893,0.00145172,0.0008432905,0.0005958059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007342555,"about_ca_system_score_gemma":0.0006293704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008442677,"about_ca_topic_score_gemma":0.01418309,"domain_scores_codex":[0.9991521,0.0002953919,0.00003054363,0.0002025718,0.0001719475,0.0001474996],"domain_scores_gemma":[0.9955305,0.00272369,0.0006868176,0.0003288396,0.0003764657,0.0003537303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001785645,0.0009917177,0.6706818,0.0001792627,0.0005132016,0.0002328894,0.000988339,0.1042482,0.004192512,0.003510954,0.002490392,0.210185],"study_design_scores_gemma":[0.00004106394,0.0005383515,0.4496641,0.0000708753,0.0002085745,0.0001314503,0.0005618351,0.5374634,0.001280726,0.008618231,0.001369282,0.00005211991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608125,0.0006022903,0.03527732,0.000260499,0.00003724765,0.00006300223,0.0002923257,0.0001544759,0.002500351],"genre_scores_gemma":[0.9888592,0.00009032888,0.009542465,0.0000410577,0.00001454136,0.00003770081,0.0004023588,0.00001840913,0.0009939339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008442677,"threshold_uncertainty_score":0.01927215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0710171355124739,"score_gpt":0.2589473709587483,"score_spread":0.1879302354462744,"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."}}