{"id":"W15976199","doi":"10.1213/01.ane.0000155260.93406.29","title":"Automatic camera control using unobtrusive vision and audio tracking","year":2010,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"National Heart, Lung, and Blood Institute","keywords":"Computer science; Video production; Video tracking; Variety (cybernetics); Video processing; Tracking (education); Post-production; Multimedia; Computer vision; Key (lock); Quality (philosophy); Artificial intelligence; Video quality; Video camera; Engineering","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":[0.0003838208,0.0001308139,0.0001935217,0.0001955378,0.000199588,0.0003510973,0.0003471672,0.00008797932,0.00001849843],"category_scores_gemma":[0.00009419984,0.0001122283,0.00006923,0.0004224438,0.00007713522,0.0005433778,0.0001129262,0.0002746634,0.000005524791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001185244,"about_ca_system_score_gemma":0.00002796424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006069262,"about_ca_topic_score_gemma":0.00008115404,"domain_scores_codex":[0.9989947,0.00006430825,0.0002409051,0.0003134851,0.0002041113,0.0001824765],"domain_scores_gemma":[0.9991869,0.0001030306,0.0001334953,0.0003680236,0.0001334058,0.00007515905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000018108,0.0003260222,0.06150542,0.0001288405,0.00036192,0.00005196606,0.007197759,0.005657708,0.414198,0.2824095,0.0004033549,0.2277414],"study_design_scores_gemma":[0.000255202,0.00004175152,0.005211997,0.00003584651,0.00002804409,0.00002463328,0.00002338325,0.9884294,0.002204139,0.00337346,0.0002274586,0.0001447411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4707542,0.00007590088,0.5284682,0.0003060951,0.0002216716,0.00006002846,9.26652e-7,0.0000550591,0.00005786018],"genre_scores_gemma":[0.989971,0.00001524167,0.009715651,0.0002408873,0.00003164695,0.000001884842,7.25994e-7,0.000008037138,0.00001494088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9827716,"threshold_uncertainty_score":0.4576538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113881727990213,"score_gpt":0.2750508403293684,"score_spread":0.2636626675303471,"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."}}