{"id":"W1977383060","doi":"10.1109/ism.2011.58","title":"Shot Boundary Detection Using Genetic Algorithm Optimization","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Computer science; Metric (unit); Genetic algorithm; Shot (pellet); Convergence (economics); Heuristic; Boundary (topology); Enhanced Data Rates for GSM Evolution; Precision and recall; Edge detection; Artificial intelligence; Algorithm; Pattern recognition (psychology); Computer vision; Image (mathematics); Machine learning; Image processing; Mathematics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009817976,0.00006884027,0.00007249197,0.0001185701,0.0001527549,0.0001256569,0.0001997129,0.00004191049,0.0001266209],"category_scores_gemma":[0.000007373908,0.00006310979,0.00004256725,0.0004174519,0.00001438857,0.0004881131,0.00006348784,0.00003519489,0.00001381751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003089559,"about_ca_system_score_gemma":0.00002996626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001682548,"about_ca_topic_score_gemma":0.00001441005,"domain_scores_codex":[0.9993259,0.00003901005,0.0001615239,0.0002163927,0.0001394603,0.0001177649],"domain_scores_gemma":[0.9995674,0.000005204819,0.00005387492,0.0002440406,0.00008678515,0.00004271204],"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":[0.000003209749,0.0001201619,0.002326862,0.000005194236,0.00005150834,0.00001044282,0.0008097735,0.1461708,0.001374528,0.001563778,0.00002590356,0.8475379],"study_design_scores_gemma":[0.0000630071,0.00002706862,0.002880254,0.000001786489,0.00001112076,0.000007906762,0.00000863915,0.9941534,0.002216769,0.0004795298,0.0000628082,0.00008766596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003174898,0.00002971354,0.9940256,0.000009708954,0.0001866275,0.00005065664,1.731193e-7,0.0001016243,0.00242098],"genre_scores_gemma":[0.2034374,0.00001043288,0.7963279,0.00008610239,0.00003468021,0.000001922785,0.000001676819,0.000004896086,0.00009492198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8479826,"threshold_uncertainty_score":0.2573543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0385976578761106,"score_gpt":0.2327165130138793,"score_spread":0.1941188551377687,"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."}}