{"id":"W2949987926","doi":"10.48550/arxiv.1610.00318","title":"MinMax Radon Barcodes for Medical Image Retrieval","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Radon; Minimax; Thresholding; Computer science; Binary number; Image (mathematics); Pattern recognition (psychology); Artificial intelligence; Image retrieval; Feature (linguistics); Computer vision; Mathematics; Mathematical optimization; Arithmetic; Physics","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":[0.001554699,0.001074442,0.001299421,0.003230483,0.0004610023,0.001892906,0.001279928,0.001343791,0.007089598],"category_scores_gemma":[0.009515481,0.0003679035,0.001022181,0.002924148,0.0007892641,0.002191662,0.001272919,0.001412734,0.005366818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008494378,"about_ca_system_score_gemma":0.001205388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002188137,"about_ca_topic_score_gemma":0.001921758,"domain_scores_codex":[0.9982082,0.0003506572,0.0001683635,0.0003301318,0.0008170482,0.0001256847],"domain_scores_gemma":[0.9971463,0.001026435,0.0004859612,0.0005801928,0.0006718497,0.00008927933],"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":[0.0006936627,0.00009667651,0.001320805,0.0004576926,0.00007640634,0.0001265516,0.00008793972,0.01739564,0.04370171,0.01481931,0.008497463,0.9127261],"study_design_scores_gemma":[0.00008410004,0.0008659025,0.008116365,0.0002302003,0.0001400292,0.002259746,0.0001967033,0.7292525,0.176055,0.02982876,0.05275409,0.0002165531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01889885,0.003521338,0.9688798,0.0004172523,0.000244076,0.0001932762,0.0009749991,0.004621825,0.002248531],"genre_scores_gemma":[0.2214634,0.002990886,0.7639076,0.0003878334,0.0003446126,0.0003930247,0.003261667,0.0005096158,0.006741414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007089598,"threshold_uncertainty_score":0.02371705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05725433636840206,"score_gpt":0.2184590075408843,"score_spread":0.1612046711724823,"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."}}