{"id":"W2012584471","doi":"10.1109/icmew.2012.102","title":"A Textural Based Hidden Markov Model for Animation Genre Discrimination","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hidden Markov model; Computer science; Categorization; Animation; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Feature vector; Feature extraction; Markov chain; Computer vision; Computer graphics (images); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009206741,0.000576495,0.0006125123,0.001116216,0.0003759185,0.000656686,0.001140875,0.00076175,0.00296121],"category_scores_gemma":[0.002206834,0.0003519001,0.0009455979,0.0007128678,0.0002972205,0.0009889662,0.0003691282,0.00110367,0.001460629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008275782,"about_ca_system_score_gemma":0.0007208762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01306433,"about_ca_topic_score_gemma":0.0138032,"domain_scores_codex":[0.9996424,0.00007584321,0.00002855878,0.0001260187,0.00008192805,0.00004521016],"domain_scores_gemma":[0.9992361,0.0004927228,0.00006278126,0.0000558836,0.0001243776,0.00002814784],"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":[0.000885357,0.0005482095,0.01373145,0.0003089224,0.0003345808,0.0004667913,0.0003764022,0.3692896,0.0310383,0.02800905,0.009423872,0.5455875],"study_design_scores_gemma":[0.00000948829,0.00003422888,0.001127286,0.00001031625,0.00002599515,0.0000467811,0.00001110409,0.9941241,0.001236409,0.002620569,0.000739892,0.00001381688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04269522,0.0007657605,0.9509015,0.0003575398,0.0001927219,0.0001265027,0.001066784,0.001611622,0.002282239],"genre_scores_gemma":[0.7146543,0.000991008,0.2701951,0.0002426828,0.0002150258,0.0003877972,0.002896684,0.0001761269,0.0102413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01306433,"threshold_uncertainty_score":0.02597654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02994816388686817,"score_gpt":0.2672181499239211,"score_spread":0.237269986037053,"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."}}