{"id":"W4242794374","doi":"10.32920/ryerson.14651541.v1","title":"Robust Image Labeling Using Conditional Random Fields","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conditional random field; Artificial intelligence; Discriminative model; Computer science; Graphical model; Pattern recognition (psychology); Feature (linguistics); Cognitive neuroscience of visual object recognition; Semantics (computer science); Mixture model; Machine learning; Computer vision; Object (grammar)","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":[0.00206424,0.001069566,0.001473439,0.001875063,0.0006445865,0.001239203,0.00273601,0.001590968,0.002582906],"category_scores_gemma":[0.005420698,0.000979943,0.001836971,0.001552229,0.001509716,0.002327361,0.001448108,0.002091788,0.00111369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002194755,"about_ca_system_score_gemma":0.001609586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01016539,"about_ca_topic_score_gemma":0.009638444,"domain_scores_codex":[0.9986603,0.0003688411,0.00005271379,0.0004958358,0.0002929381,0.0001292273],"domain_scores_gemma":[0.9969242,0.001714076,0.0003650302,0.000549831,0.0003583027,0.00008850217],"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":[0.0001358575,0.00004869011,0.0006607135,0.000119035,0.00006344818,0.00009374155,0.00007824015,0.8150049,0.004172442,0.02980437,0.003571744,0.1462468],"study_design_scores_gemma":[0.000003741566,0.000007680132,0.00006250334,0.000005453051,0.000004548371,0.00001471667,0.00000307301,0.9866958,0.0005914745,0.01223085,0.000373979,0.000006188091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003108025,0.0001234579,0.9952284,0.00009250239,0.00001635836,0.00002285192,0.00008104376,0.0009078709,0.0004194901],"genre_scores_gemma":[0.3599299,0.0005556599,0.6321666,0.0003602123,0.0001651263,0.0002616966,0.00191875,0.0006999761,0.003942198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01016539,"threshold_uncertainty_score":0.02021247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06905511196490598,"score_gpt":0.2943899235751589,"score_spread":0.2253348116102529,"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."}}