{"id":"W4240506654","doi":"10.1109/cvprw.2009.5206839","title":"Picking the best DAISY","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Normalization (sociology); Scale-invariant feature transform; Pattern recognition (psychology); Byte; Computer vision; Feature extraction","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":[0.0005345891,0.0009434026,0.001512177,0.001239544,0.0008441915,0.001285754,0.001354952,0.001116279,0.008056037],"category_scores_gemma":[0.00368525,0.0005752657,0.0006080919,0.001327599,0.0008701236,0.002625736,0.001867588,0.0009942102,0.003184506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005643481,"about_ca_system_score_gemma":0.0009700079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002860791,"about_ca_topic_score_gemma":0.005423611,"domain_scores_codex":[0.9993917,0.00007709218,0.00003125136,0.0002334202,0.0001426223,0.000124037],"domain_scores_gemma":[0.9990884,0.0001359793,0.00008081177,0.0003197085,0.000262519,0.0001125013],"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":[0.001640348,0.000864029,0.01298228,0.0003616313,0.0002215747,0.0005572035,0.0002719722,0.04353338,0.1546092,0.00934999,0.02526212,0.7503463],"study_design_scores_gemma":[0.00061184,0.002299032,0.01414214,0.00009278158,0.0003198909,0.001841504,0.001606736,0.6573242,0.2533895,0.03425228,0.03392579,0.0001944215],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5947397,0.001688166,0.3782471,0.001273194,0.0003159858,0.0004828068,0.001097005,0.008362318,0.01379363],"genre_scores_gemma":[0.8416126,0.0004552809,0.1475948,0.0005332126,0.00006333835,0.0001847544,0.001715826,0.0007239516,0.00711614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008056037,"threshold_uncertainty_score":0.02695018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0598398514029056,"score_gpt":0.3177254451712137,"score_spread":0.2578855937683081,"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."}}