{"id":"W2002632913","doi":"10.4018/jdls.2010040103","title":"A Bayesian Image Retrieval Framework","year":2010,"lang":"en","type":"article","venue":"International Journal of Digital Library Systems","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Information retrieval; Relevance (law); Exploit; Context (archaeology); Bayesian probability; Relevance feedback; Image retrieval; Relation (database); Process (computing); Visual Word; Image (mathematics); Artificial intelligence; Data mining","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":[0.003507074,0.001025254,0.001885147,0.003259872,0.0007785503,0.003091052,0.003461326,0.002676141,0.004990493],"category_scores_gemma":[0.008052079,0.0008235631,0.001244027,0.0033907,0.001314229,0.004267057,0.001561689,0.001470832,0.003116501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001851153,"about_ca_system_score_gemma":0.002052478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009479349,"about_ca_topic_score_gemma":0.00749233,"domain_scores_codex":[0.997318,0.0008676676,0.0001429423,0.0004444333,0.001067003,0.0001600026],"domain_scores_gemma":[0.9980962,0.0008765735,0.0001646368,0.0001903675,0.0005963545,0.00007583959],"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":[0.0002182098,0.0002113625,0.001051071,0.0007185032,0.000256396,0.0003541472,0.0003202802,0.2570925,0.008108531,0.3443137,0.01786311,0.3694923],"study_design_scores_gemma":[0.00005330366,0.0001027959,0.00050461,0.00006564946,0.00009535312,0.0004316714,0.00005085058,0.8570541,0.001395896,0.1231015,0.01706066,0.00008370175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001731373,0.001748926,0.9910116,0.0005341911,0.00005256149,0.0001103609,0.0002011632,0.0003480398,0.004261748],"genre_scores_gemma":[0.2214867,0.007379728,0.7472226,0.0009465823,0.000927151,0.0007612156,0.001348533,0.0001793901,0.0197481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009479349,"threshold_uncertainty_score":0.01884836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007534973828554158,"score_gpt":0.2435961766269306,"score_spread":0.2360612027983765,"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."}}