{"id":"W2338012757","doi":"10.1145/1279540.1279551","title":"Emotive captioning","year":2007,"lang":"en","type":"article","venue":"Computers in entertainment","topic":"Subtitles and Audiovisual Media","field":"Arts and Humanities","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Closed captioning; Emotive; Prosody; Computer science; CLIPS; Multimedia; Style (visual arts); Psychology; Linguistics; Speech recognition; Artificial intelligence; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001706057,0.0000748152,0.00008527238,0.00008371612,0.00006072561,0.00004417556,0.00008646921,0.00001411274,0.0003966475],"category_scores_gemma":[0.000005095505,0.00006697364,0.00003602401,0.00001892426,0.00005856849,0.0000520929,0.00004128489,0.00006940531,0.00007958285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009029233,"about_ca_system_score_gemma":0.000004529867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007217202,"about_ca_topic_score_gemma":0.0003770519,"domain_scores_codex":[0.9994037,0.00001185702,0.0001542062,0.0001234679,0.0001107212,0.0001960206],"domain_scores_gemma":[0.999774,0.00006210236,0.00003188623,0.00007717957,0.00001371877,0.00004115164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001075502,0.0006982692,0.02401416,0.00007041074,0.0001104802,0.000338222,0.3305009,0.0002661466,0.0002442921,0.4014766,0.01410192,0.2280711],"study_design_scores_gemma":[0.003568509,0.0006531879,0.04949677,0.0008211283,0.00002885745,0.00001776483,0.06640059,0.003307725,0.001203864,0.007042217,0.8663658,0.001093572],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9081103,0.0001195917,0.008352214,0.0002437256,0.003157191,0.000169299,0.000002172377,0.00005873304,0.07978683],"genre_scores_gemma":[0.9978024,0.00000552393,0.0004874277,0.0005711626,0.0004498804,0.000002899863,0.000004804426,0.000007481032,0.0006684022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8522639,"threshold_uncertainty_score":0.4343013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02226795564990491,"score_gpt":0.2446527313945649,"score_spread":0.22238477574466,"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."}}