{"id":"W2117962700","doi":"10.1016/j.cviu.2007.04.006","title":"Performance characterization in computer vision: A guide to best practices","year":2007,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Variety (cybernetics); Novelty; Computer science; Field (mathematics); Domain (mathematical analysis); Artificial intelligence; Data science; Human–computer interaction; Machine learning; Management science; Emphasis (telecommunications); Mathematics; Psychology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004272,0.000255487,0.0002730794,0.0005375717,0.0002501719,0.00064949,0.0004877648,0.00008689671,0.000007380857],"category_scores_gemma":[0.00004222072,0.0002317,0.00004508115,0.0008487987,0.00006511853,0.003373131,0.0006713379,0.0002263825,0.00002940964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002282863,"about_ca_system_score_gemma":0.00002979775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006961918,"about_ca_topic_score_gemma":0.000005933266,"domain_scores_codex":[0.9979773,0.00006217899,0.0005010685,0.0006677379,0.0003365735,0.0004551314],"domain_scores_gemma":[0.9987885,0.0002038057,0.0002757588,0.0004184036,0.0001079551,0.0002056283],"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":[0.0001177201,0.0001770426,0.001124129,0.00008233837,0.000009927874,0.0002051284,0.0009849005,0.00002253177,0.03720988,0.004056786,0.001275732,0.9547339],"study_design_scores_gemma":[0.002356973,0.00490647,0.03387629,0.001787228,0.00001692503,0.0004555651,0.0002523123,0.8730933,0.02434573,0.001959236,0.0553156,0.001634372],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02539968,0.00003785165,0.9719445,0.0009492205,0.0003593677,0.0003383683,0.000001422314,0.0002231485,0.0007464918],"genre_scores_gemma":[0.3882965,0.0002376995,0.6095476,0.001519444,0.0002410549,0.000003686418,0.000008666292,0.00002088632,0.0001245391],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9530995,"threshold_uncertainty_score":0.944845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05104033449093394,"score_gpt":0.364675211314213,"score_spread":0.3136348768232791,"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."}}