{"id":"W298976848","doi":"10.1007/978-3-319-10599-4_34","title":"Discovering Video Clusters from Visual Features and Noisy Tags","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Cluster analysis; Consistency (knowledge bases); Margin (machine learning); Artificial intelligence; Visualization; Pattern recognition (psychology); Information retrieval; Machine learning","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005639436,0.0004850691,0.000584635,0.0006127693,0.000290449,0.001295125,0.001807372,0.0002900509,0.00001063856],"category_scores_gemma":[0.00008959645,0.0004185174,0.0001311503,0.0004147416,0.0004217657,0.0006754294,0.001578656,0.0005502959,0.00001501759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001431963,"about_ca_system_score_gemma":0.0001646882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001967808,"about_ca_topic_score_gemma":0.0004999649,"domain_scores_codex":[0.996564,0.00004649259,0.0004495279,0.001607712,0.0008604781,0.0004718052],"domain_scores_gemma":[0.9980454,0.0004464938,0.0002737046,0.0009355065,0.000118307,0.0001806036],"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":[0.000008836754,0.00001755366,0.0005035721,0.00003340143,0.00004079254,0.00004529881,0.00135534,0.04407272,0.0004886989,0.01407761,0.00008392247,0.9392722],"study_design_scores_gemma":[0.0002729261,0.0001131695,0.001306636,0.0003235933,0.00002480281,0.00002216573,3.341698e-7,0.948065,0.0006904015,0.04714251,0.001311607,0.0007268037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008223028,0.000503882,0.9958526,0.0007847527,0.0009884109,0.0001719817,0.000004679193,0.00009477684,0.0007765646],"genre_scores_gemma":[0.7857959,0.0001344511,0.2087757,0.003529752,0.001004066,0.000006747433,0.00003224998,0.00005118442,0.0006699536],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9385455,"threshold_uncertainty_score":0.9998267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007257881268353208,"score_gpt":0.2272532916326793,"score_spread":0.2199954103643261,"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."}}