{"id":"W2953660811","doi":"10.21437/interspeech.2019-3062","title":"Analyzing Verbal and Nonverbal Features for Predicting Group Performance","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of the Fraser Valley","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonverbal communication; Task (project management); Conversation; Computer science; Feature (linguistics); Natural language processing; Artificial intelligence; Psychology; Cognitive psychology; Speech recognition; Communication; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004698797,0.0002535074,0.000344872,0.0001202578,0.0001262638,0.0004333022,0.0008067345,0.0002612769,0.00000250299],"category_scores_gemma":[0.00003994614,0.0002183022,0.0001224559,0.0001012767,0.00002613774,0.000313294,0.001228823,0.0002664636,0.0000103717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005169555,"about_ca_system_score_gemma":0.000100457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002505152,"about_ca_topic_score_gemma":0.00007074113,"domain_scores_codex":[0.9983701,0.00003170807,0.000275252,0.0007362938,0.0002170333,0.0003696179],"domain_scores_gemma":[0.9988442,0.0001379657,0.0001738649,0.0006606056,0.00007421966,0.0001091192],"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":[0.0002042354,0.0001742395,0.7968696,0.007265666,0.0005216744,0.00002228842,0.00476455,0.004439133,0.001190315,0.04513582,0.0249491,0.1144634],"study_design_scores_gemma":[0.001742069,0.0003936838,0.1170089,0.0007061693,0.00006426453,0.00008012767,0.0001215771,0.8706635,0.001820237,0.001973525,0.004189965,0.001235986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.326109,0.0009151323,0.6604533,0.0002051987,0.004999117,0.001100903,0.00002763122,0.0003989022,0.005790792],"genre_scores_gemma":[0.9634747,0.00003855244,0.03508158,0.00007968189,0.0005455077,0.00005784861,0.00003470345,0.00001490017,0.0006725549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8662243,"threshold_uncertainty_score":0.8902105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410929847238717,"score_gpt":0.2362206573028312,"score_spread":0.222111358830444,"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."}}