{"id":"W4404445945","doi":"10.4018/ijswis.359768","title":"Multi Frame Obscene Video Detection With ViT","year":2024,"lang":"en","type":"article","venue":"International Journal on Semantic Web and Information Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Frame (networking); Speech recognition; Multimedia; Telecommunications","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":[0.0006628932,0.001284152,0.001333182,0.003792369,0.0004483199,0.001446104,0.001145236,0.001042765,0.002184524],"category_scores_gemma":[0.002344415,0.0003018052,0.0008482602,0.001678751,0.0004486352,0.001361624,0.001191663,0.001228637,0.002492547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005642629,"about_ca_system_score_gemma":0.0007678882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004756021,"about_ca_topic_score_gemma":0.005519854,"domain_scores_codex":[0.9992033,0.00007594638,0.00005045888,0.0002402112,0.0003023217,0.0001278229],"domain_scores_gemma":[0.999164,0.0001437947,0.0001297936,0.0001905342,0.0003115054,0.00006039672],"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":[0.001016512,0.0002244205,0.005237844,0.0005049198,0.0001538047,0.0004005849,0.0001632148,0.009256488,0.09690007,0.002468236,0.02156069,0.8621133],"study_design_scores_gemma":[0.0001051291,0.0007678823,0.0162835,0.0002045728,0.0002845643,0.002081587,0.0004718951,0.7514585,0.1816432,0.006941855,0.03964623,0.0001111519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1115562,0.006238272,0.8515595,0.000657515,0.0007699208,0.0007896183,0.004565752,0.01293802,0.0109252],"genre_scores_gemma":[0.4906625,0.004201969,0.4733837,0.0005123038,0.0007236763,0.0003038165,0.01410154,0.000672163,0.01543824],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004756021,"threshold_uncertainty_score":0.009456694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008566037662878046,"score_gpt":0.233516258240884,"score_spread":0.224950220578006,"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."}}