{"id":"W2024437575","doi":"10.1155/asp/2006/49073","title":"Information Mining from Multimedia Databases","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Multimedia; Database; Information retrieval; World Wide Web","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":[0.003383336,0.001115347,0.001699226,0.008335983,0.0009230315,0.004891276,0.002289588,0.00202952,0.01854969],"category_scores_gemma":[0.01376351,0.0005958013,0.001440623,0.008645931,0.0006774944,0.007060756,0.002532032,0.00308356,0.008617344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007753659,"about_ca_system_score_gemma":0.001008634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008071235,"about_ca_topic_score_gemma":0.00107525,"domain_scores_codex":[0.9970737,0.000673811,0.0003612864,0.0004702712,0.001302874,0.0001180642],"domain_scores_gemma":[0.9915301,0.004144377,0.0004571656,0.0009701622,0.002533406,0.0003648854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001029461,0.00008168866,0.0005934801,0.0009333693,0.000147299,0.0002340355,0.00006397377,0.001314756,0.0009946715,0.01068215,0.4611191,0.5237327],"study_design_scores_gemma":[0.00004659173,0.00009013025,0.001115097,0.0006503008,0.0001041158,0.0005944893,0.0001799042,0.01954352,0.002723834,0.0427522,0.9321444,0.0000554444],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006553784,0.1926455,0.5604979,0.08396974,0.07167372,0.001746235,0.02047226,0.009197368,0.05324355],"genre_scores_gemma":[0.05982393,0.1716076,0.4747733,0.03017801,0.06787609,0.002134832,0.08997519,0.002302886,0.1013281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01854969,"threshold_uncertainty_score":0.06205487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636178534727173,"score_gpt":0.2767884306344733,"score_spread":0.2604266452872016,"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."}}