{"id":"W7126440079","doi":"10.21428/594757db.dd360009","title":"Multi-modal News Understanding with Professionally LabelledVideos (ReutersViLNews)","year":2024,"lang":"en","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada); Vector Institute; University of British Columbia","funders":"","keywords":"Event (particle physics); Context (archaeology); Subject (documents); Action (physics); Benchmark (surveying); Domain (mathematical analysis); Big data","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00191906,0.003646607,0.001222388,0.01017835,0.001313474,0.002906279,0.002941193,0.003988744,0.009555816],"category_scores_gemma":[0.008030897,0.0006261118,0.00216041,0.004707361,0.0007145841,0.004953166,0.00345956,0.0027222,0.01130765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002092506,"about_ca_system_score_gemma":0.001329497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03735768,"about_ca_topic_score_gemma":0.0655601,"domain_scores_codex":[0.9969587,0.000622477,0.0002460479,0.0009972053,0.0007852088,0.0003904071],"domain_scores_gemma":[0.9966467,0.00111376,0.0003035922,0.0009820053,0.0007290826,0.0002248781],"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":[0.001480251,0.001125617,0.00713922,0.004375315,0.0005785469,0.001324099,0.001392181,0.01004178,0.0185589,0.003956442,0.655463,0.2945647],"study_design_scores_gemma":[0.0004331129,0.0006363533,0.0366471,0.001356298,0.000494696,0.001948021,0.004184412,0.1816773,0.03506161,0.009602566,0.7275054,0.0004531678],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.067187,0.006677821,0.05814309,0.00174993,0.001342125,0.001760093,0.7717604,0.05916267,0.03221686],"genre_scores_gemma":[0.03948475,0.0006648464,0.05983684,0.000340157,0.0002181153,0.0006324389,0.8929505,0.0009910403,0.004881308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03735768,"threshold_uncertainty_score":0.0742805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04817114733350052,"score_gpt":0.311692875112794,"score_spread":0.2635217277792935,"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."}}