{"id":"W2076846377","doi":"10.1016/j.ipm.2012.12.001","title":"CIDER: Concept-based image diversification, exploration, and retrieval","year":2013,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina; Memorial University of Newfoundland","funders":"","keywords":"Information retrieval; Computer science; Image retrieval; Query expansion; Visual Word; Web query classification; Key (lock); Automatic image annotation; Filter (signal processing); Web search query; Image (mathematics); Search engine; Artificial intelligence; Computer vision","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":[0.00273154,0.001092071,0.001947572,0.00379145,0.0008678132,0.001777585,0.002944834,0.001455556,0.00666902],"category_scores_gemma":[0.00518958,0.0005240861,0.001080601,0.002653084,0.0008960198,0.002496504,0.003390344,0.001770036,0.001805209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001281664,"about_ca_system_score_gemma":0.001794947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003146218,"about_ca_topic_score_gemma":0.004588563,"domain_scores_codex":[0.9985091,0.0002373265,0.00006923498,0.0002348503,0.0008009992,0.0001484146],"domain_scores_gemma":[0.9981636,0.0007009825,0.0001160515,0.0003677668,0.000493185,0.0001584496],"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":[0.000895856,0.0004772639,0.0009354904,0.0004350555,0.0001476335,0.0001771578,0.0001497488,0.03190924,0.03703111,0.02441623,0.03215742,0.8712678],"study_design_scores_gemma":[0.0003434121,0.0004194644,0.001026294,0.00005700851,0.0001092574,0.0006989078,0.0001002596,0.8889999,0.05460821,0.02450243,0.0290235,0.0001114205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01034974,0.00183515,0.9721498,0.0001925148,0.0001498212,0.000422602,0.0004789275,0.01131925,0.003102142],"genre_scores_gemma":[0.1018037,0.0005875123,0.8921343,0.0002915926,0.00008477957,0.0002672277,0.0008536213,0.0004655155,0.003511769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00666902,"threshold_uncertainty_score":0.02231008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01259878672133456,"score_gpt":0.2497941533249294,"score_spread":0.2371953666035949,"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."}}