{"id":"W4400975256","doi":"10.1109/access.2024.3433395","title":"GenVis: Visualizing Genre Detection in Movie Trailers for Enhanced Understanding","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Visualization; Film genre; Timeline; Usability; Depiction; Natural language processing; Information retrieval; sort; Artificial intelligence; Human–computer interaction; Movie theater","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":[0.0002904278,0.00008963803,0.0001191938,0.0002984749,0.00009887258,0.0007368599,0.0003815806,0.00005449774,0.000005562459],"category_scores_gemma":[0.00002007872,0.0000858361,0.00008082169,0.0008671539,0.000009837438,0.001304225,0.00003905391,0.00006866258,0.000005386442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001903484,"about_ca_system_score_gemma":0.00003605255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004251181,"about_ca_topic_score_gemma":0.0004146073,"domain_scores_codex":[0.9990757,0.00003308057,0.0002130231,0.0003442302,0.0001456892,0.0001883077],"domain_scores_gemma":[0.9996547,0.00008776988,0.0000392314,0.0001600154,0.0000239103,0.00003436295],"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":[0.00003202536,0.00008606827,0.0005459217,0.0004434329,0.0001874254,0.00003561515,0.005704813,0.02580871,0.328146,0.04706471,0.0005746378,0.5913706],"study_design_scores_gemma":[0.0002084256,0.00002888825,0.0004918678,0.00009191796,0.00001849924,0.000001376681,0.0001441024,0.8920771,0.09177873,0.01459122,0.0003708481,0.0001969733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05864479,0.0001637538,0.9397724,0.0001593771,0.000699976,0.0001494891,0.000001127529,0.0001201381,0.0002889588],"genre_scores_gemma":[0.9989615,0.00003789111,0.0006715304,0.0001135213,0.00009683332,0.00004275762,0.000002724518,0.000010307,0.00006292768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9403167,"threshold_uncertainty_score":0.7105557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09257614238437267,"score_gpt":0.358068763946263,"score_spread":0.2654926215618904,"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."}}