{"id":"W2956357266","doi":"10.3390/mti3030054","title":"Graph-Based Prediction of Meeting Participation","year":2019,"lang":"en","type":"article","venue":"Multimodal Technologies and Interaction","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"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; Baseline (sea); Computer science; Task (project management); Feature (linguistics); Point (geometry); Predictive modelling; Artificial intelligence; Machine learning; Graph; Cognitive psychology; Natural language processing; Psychology; Developmental psychology; Mathematics; Linguistics; Engineering; Theoretical computer science","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":[0.0001271341,0.00006899778,0.0001030778,0.0001638111,0.0000358469,0.00003520348,0.0001453923,0.00009118073,0.000004417528],"category_scores_gemma":[0.000103328,0.0000592429,0.00003296526,0.0001880077,0.00002821682,0.0003954542,0.00006152773,0.00008597594,0.00001425804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002192784,"about_ca_system_score_gemma":0.000008329796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001502376,"about_ca_topic_score_gemma":0.000008001304,"domain_scores_codex":[0.9993854,0.00002076558,0.0001887014,0.0001909808,0.0001031875,0.0001109793],"domain_scores_gemma":[0.9995036,0.00005950419,0.0001306408,0.0002332136,0.00005939017,0.00001361777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009233379,0.0001616798,0.1129684,0.0001135327,0.00003151465,0.000001415532,0.0003423939,0.002287176,0.2218046,0.007385763,0.0002156351,0.6545956],"study_design_scores_gemma":[0.0006292951,0.0004752287,0.04030769,0.0001335235,0.000005689033,0.000003895118,0.0007535982,0.6651601,0.2902225,0.001258495,0.0009240834,0.0001259503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.942133,0.00006645064,0.05543118,0.0002675075,0.0006049373,0.0001933492,0.000002987786,0.0005275061,0.0007730853],"genre_scores_gemma":[0.9934903,0.00001258614,0.006439792,0.00001081802,0.00001000061,0.00001818005,0.000004302824,0.000002986553,0.00001102063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6628729,"threshold_uncertainty_score":0.2415855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957823932293072,"score_gpt":0.256861156871655,"score_spread":0.2372829175487243,"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."}}