{"id":"W2013474292","doi":"10.1109/icip.2014.7026187","title":"A new robust context-based dense CRF model for image labeling","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Context (archaeology); Image (mathematics); Artificial intelligence; Context model; Computer vision; Geology","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":[0.00102993,0.0007448458,0.00121436,0.0009810691,0.0005390409,0.0007367974,0.003057791,0.001399791,0.002475287],"category_scores_gemma":[0.003160791,0.000885699,0.001244718,0.001775142,0.0007045359,0.002342954,0.0008442413,0.001856706,0.0009821132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00122002,"about_ca_system_score_gemma":0.001578599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01441473,"about_ca_topic_score_gemma":0.02174165,"domain_scores_codex":[0.9992336,0.0001831026,0.00003826253,0.0002863538,0.0001901182,0.00006861006],"domain_scores_gemma":[0.9990891,0.000386744,0.00009171163,0.0001993793,0.0002024602,0.00003054183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001817235,0.00007511696,0.0009642813,0.0002212806,0.0001030009,0.0002023458,0.0001479215,0.7028553,0.009829378,0.03847667,0.01183196,0.235111],"study_design_scores_gemma":[0.00000741435,0.00001186898,0.0001381075,0.000006633642,0.0000116668,0.00004530402,0.000004350188,0.9870418,0.0009414713,0.01032032,0.001459331,0.00001156817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002675537,0.0001984651,0.9954875,0.00009659716,0.00002986539,0.00002656305,0.0002208303,0.000798442,0.0004662322],"genre_scores_gemma":[0.2088081,0.0005673576,0.7832139,0.0003108875,0.0001283115,0.0002523689,0.002050997,0.0004307047,0.004237457],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01441473,"threshold_uncertainty_score":0.02866167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03829187739217151,"score_gpt":0.2665526586028363,"score_spread":0.2282607812106648,"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."}}