{"id":"W2136771689","doi":"10.1109/tvcg.2006.194","title":"Visual Signatures in Video Visualization","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Society","keywords":"Visualization; Computer science; Data visualization; Visual analytics; Pipeline (software); Information visualization; Set (abstract data type); Interactive visual analysis; Process (computing); Computer vision; Artificial intelligence; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002278351,0.0009300108,0.0007319839,0.002479065,0.000804576,0.00457548,0.00130781,0.001952686,0.005340586],"category_scores_gemma":[0.01668129,0.0006457179,0.0005457745,0.002539392,0.002920703,0.00483848,0.002564574,0.002069018,0.001275609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009132404,"about_ca_system_score_gemma":0.0006890938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001678614,"about_ca_topic_score_gemma":0.0008452266,"domain_scores_codex":[0.9970914,0.001454215,0.0001450809,0.0003652067,0.0008132374,0.0001307038],"domain_scores_gemma":[0.9931905,0.004318927,0.0005004662,0.0007792127,0.0009400902,0.0002708017],"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":[0.0004104702,0.00008529084,0.00183562,0.001933521,0.00007257708,0.000675779,0.0023868,0.02368612,0.03110216,0.2780534,0.01934156,0.6404167],"study_design_scores_gemma":[0.0001369222,0.0005620164,0.003754088,0.001185232,0.0001172525,0.004150977,0.001852387,0.1872482,0.04962465,0.5241211,0.2269193,0.0003277875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01296222,0.01435805,0.9484112,0.003108235,0.0006933276,0.0002409566,0.0002580248,0.001547067,0.01842093],"genre_scores_gemma":[0.2997334,0.01547032,0.6738393,0.0009122145,0.001167541,0.0004573314,0.0004246158,0.0005644452,0.007430778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005340586,"threshold_uncertainty_score":0.01786602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009063503889180799,"score_gpt":0.2561166880877572,"score_spread":0.2470531841985764,"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."}}