{"id":"W4401484758","doi":"10.1016/j.softx.2024.101845","title":"Corrigendum to “FT Xtraction: Feature Extraction and Visualization of Conversational Video Data for Social and Emotional Analysis” [SoftwareX Volume 27 (2024), 1-8, 101827]","year":2024,"lang":"en","type":"erratum","venue":"SoftwareX","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Computer science; Volume (thermodynamics); Visualization; Feature extraction; Feature (linguistics); Artificial intelligence; Natural language processing; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008142552,0.0004532429,0.0008008078,0.001183338,0.0004048723,0.0006238223,0.0007545365,0.000708702,0.0001921907],"category_scores_gemma":[0.0004863188,0.0004763492,0.0002750021,0.002107499,0.00010208,0.001209074,0.0006004408,0.0005004702,0.00002094588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609897,"about_ca_system_score_gemma":0.0005454865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009478574,"about_ca_topic_score_gemma":0.0002875203,"domain_scores_codex":[0.9962918,0.0001152733,0.0007288911,0.001571456,0.0009522218,0.0003403355],"domain_scores_gemma":[0.9974238,0.0001433936,0.0006090276,0.0008142208,0.0008266625,0.0001828707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002673023,0.00006008962,0.002581557,0.0003771007,0.001039352,0.000003635699,0.0004373965,0.0001000681,0.00002181079,0.00226122,0.9873873,0.005703744],"study_design_scores_gemma":[0.000357387,0.0001347331,0.02709538,0.0001677717,0.002198567,0.00001840307,0.00008611018,0.2626021,0.00000840147,0.002326112,0.7043188,0.0006862009],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00004334288,0.001239506,0.9754058,0.002289825,0.01853984,0.0005218185,0.001560732,0.0001970569,0.0002020457],"genre_scores_gemma":[0.01766897,0.001323234,0.07126828,0.001797794,0.01047581,0.0002313504,0.09915082,0.0002948282,0.7977889],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9041376,"threshold_uncertainty_score":0.9997688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04015975592567699,"score_gpt":0.3180027191613948,"score_spread":0.2778429632357178,"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."}}