{"id":"W4386074526","doi":"10.11159/cist23.107","title":"A Multi-Viewpoint Approach For Semantic Multimedia Documents Adaptation","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Adaptation (eye); Multimedia; Information retrieval; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001531629,0.0008945036,0.0006154604,0.001784869,0.0009706254,0.002244205,0.002001913,0.001103504,0.001958407],"category_scores_gemma":[0.002627945,0.0005551481,0.002083537,0.001362026,0.001102094,0.003457808,0.003203646,0.001841748,0.0007813817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194299,"about_ca_system_score_gemma":0.001110262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006281521,"about_ca_topic_score_gemma":0.008113004,"domain_scores_codex":[0.9979868,0.0004902799,0.0001813902,0.000457148,0.0007510469,0.0001332413],"domain_scores_gemma":[0.9989899,0.0002312421,0.00008544013,0.0003634991,0.0002332552,0.00009669095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003372451,0.0002994059,0.003311423,0.0007855709,0.0003095445,0.001788701,0.0070367,0.03779329,0.0829005,0.3265555,0.01000824,0.5288739],"study_design_scores_gemma":[0.00006763589,0.0001901106,0.003093884,0.0002958827,0.000478244,0.001751368,0.002913565,0.5786557,0.05610181,0.184457,0.171744,0.0002508072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003473422,0.0001915425,0.9929501,0.000119759,0.00004001277,0.00007122123,0.00008395219,0.0006317978,0.002438265],"genre_scores_gemma":[0.1129962,0.0004514298,0.8823029,0.00009063426,0.00004338746,0.0001306177,0.000425897,0.0002815493,0.003277397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006281521,"threshold_uncertainty_score":0.01248991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456122033154205,"score_gpt":0.2266186683210681,"score_spread":0.212057447989526,"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."}}