{"id":"W2076424778","doi":"10.1145/2168752.2168760","title":"A Generic Approach for Systematic Analysis of Sports Videos","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Probabilistic latent semantic analysis; Conditional random field; Artificial intelligence; Topic model; Bag-of-words model; Support vector machine; Pattern recognition (psychology); Categorization; Representation (politics); Field (mathematics); Probabilistic logic; Classifier (UML); Video content analysis; Histogram; Machine learning; Object (grammar); Video tracking; Image (mathematics)","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":[0.000477619,0.0001425604,0.0006005511,0.00154104,0.0001185991,0.00003582031,0.0004452204,0.0001645483,0.000004210077],"category_scores_gemma":[0.00004803428,0.0001136333,0.0001874915,0.002433657,0.00004355132,0.0001343132,0.00001441543,0.0000759359,0.000001430715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003190248,"about_ca_system_score_gemma":0.00001324248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002899152,"about_ca_topic_score_gemma":0.000003441152,"domain_scores_codex":[0.9986604,0.00005062377,0.0005819488,0.0002857017,0.0001928084,0.0002285218],"domain_scores_gemma":[0.998538,0.000124609,0.0002490871,0.0008815673,0.0001447591,0.00006193226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004900475,0.002608536,0.03036501,0.0195171,0.01688658,0.000004278455,0.003433234,0.1106746,0.002819365,0.6948255,0.0001480641,0.1186687],"study_design_scores_gemma":[0.0002152414,0.0001927011,0.0003157547,0.0005290409,0.002818346,0.00003430297,0.001326148,0.9871598,0.005584472,0.001162498,0.0002658916,0.0003957825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005331593,0.001145822,0.992503,0.00009500835,0.0001719439,0.0005975418,0.000007602282,0.0001031689,0.00004425726],"genre_scores_gemma":[0.9815701,0.0001080811,0.01782489,0.0000142216,0.00001129007,0.0003257663,0.000007229631,0.000007832585,0.0001305839],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9762385,"threshold_uncertainty_score":0.4633833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350077246559766,"score_gpt":0.2522775301339221,"score_spread":0.2287767576683244,"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."}}