{"id":"W4315815632","doi":"10.1109/icicml57342.2022.10009736","title":"Oracle Bone Inscriptions Detection Based On Standard Evaluation Metric","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Oracle; Metric (unit); Computer science; Object (grammar); Detector; Measure (data warehouse); Object detection; Data mining; Artificial intelligence; Pattern recognition (psychology); Engineering; Programming language; Telecommunications","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":[0.006957566,0.003807807,0.00260896,0.008684221,0.0007149009,0.002906561,0.002641776,0.002736809,0.002961561],"category_scores_gemma":[0.01690399,0.0004057323,0.001267272,0.00326014,0.001294324,0.003729071,0.001882537,0.0008819658,0.001609379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001895552,"about_ca_system_score_gemma":0.001851794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00959186,"about_ca_topic_score_gemma":0.0109377,"domain_scores_codex":[0.9873348,0.001421306,0.001223342,0.002184903,0.006848734,0.0009869105],"domain_scores_gemma":[0.9876099,0.003976154,0.00105909,0.001414431,0.00541867,0.0005218693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003726053,0.001083578,0.07133462,0.002029823,0.001559432,0.0007771513,0.0002054315,0.1083885,0.03395849,0.005374006,0.05349251,0.7180704],"study_design_scores_gemma":[0.000134384,0.00168482,0.01724741,0.0001188747,0.0005290527,0.001659884,0.0002192431,0.8870608,0.07819226,0.003026762,0.009958958,0.0001675743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4546491,0.01701569,0.4439414,0.0009595656,0.001772192,0.002029173,0.01087103,0.02906538,0.03969648],"genre_scores_gemma":[0.8450072,0.001831513,0.1256895,0.0003871956,0.0002392827,0.0004421115,0.01631529,0.000782736,0.00930531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00959186,"threshold_uncertainty_score":0.03679556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02379261361949881,"score_gpt":0.3016155266198952,"score_spread":0.2778229130003964,"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."}}