{"id":"W2955437849","doi":"10.3390/mti3030046","title":"Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model","year":2019,"lang":"en","type":"article","venue":"Multimodal Technologies and Interaction","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of the Fraser Valley","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gradient boosting; Computer science; Random forest; Artificial intelligence; Conversation; Nonverbal communication; Task (project management); Information overload; Set (abstract data type); Regression; Machine learning; Feature (linguistics); Baseline (sea); Graph; Natural language processing; Mean squared error; Speech recognition; Psychology; Statistics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001273088,0.0009403591,0.0006122878,0.001075465,0.0003878246,0.0007070003,0.0008761674,0.0008824196,0.001661606],"category_scores_gemma":[0.004340861,0.0003395505,0.0007982437,0.0006879167,0.0003488425,0.001080371,0.0004269182,0.0009826162,0.0005134396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006466272,"about_ca_system_score_gemma":0.000458343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01203168,"about_ca_topic_score_gemma":0.009553356,"domain_scores_codex":[0.9996345,0.0001307337,0.00001478888,0.0001099837,0.0000581734,0.00005178512],"domain_scores_gemma":[0.9981921,0.0012894,0.0001819871,0.00007387702,0.000172407,0.0000901965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006335809,0.0003628079,0.02038208,0.0001249259,0.0001801708,0.0002884798,0.0005768655,0.852859,0.004258459,0.004343226,0.003833053,0.1121574],"study_design_scores_gemma":[0.000003884863,0.00001683425,0.001221228,0.000003225152,0.00001077257,0.00001184921,0.00001061347,0.9967789,0.0001041085,0.001757052,0.00007697618,0.00000463149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5122069,0.0008989153,0.4787177,0.001709944,0.0001451062,0.0001610889,0.0009649433,0.001447377,0.003748111],"genre_scores_gemma":[0.9704514,0.0001782363,0.02731102,0.00008527983,0.0000682199,0.00009878774,0.0004121722,0.00003568324,0.00135916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01203168,"threshold_uncertainty_score":0.02392328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00811250033285786,"score_gpt":0.2420502854212333,"score_spread":0.2339377850883755,"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."}}