{"id":"W2055246903","doi":"10.1109/access.2015.2425304","title":"A Deep-Structured Fully Connected Random Field Model for Structured Inference","year":2015,"lang":"en","type":"article","venue":"IEEE Access","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Economic Development and Innovation","keywords":"Computer science; Inference; Field (mathematics); Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001813439,0.0002779311,0.0003595087,0.0001271537,0.0001464493,0.0008382631,0.002566484,0.0001809792,0.00001858057],"category_scores_gemma":[0.0004709564,0.0002294501,0.00009351717,0.0004754719,0.00004688464,0.001417441,0.0002514083,0.0001967255,0.000008159439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003841254,"about_ca_system_score_gemma":0.0004557849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004297941,"about_ca_topic_score_gemma":0.0002009935,"domain_scores_codex":[0.9981562,0.00004061963,0.000369983,0.0005975724,0.0003475945,0.0004879976],"domain_scores_gemma":[0.9980263,0.0002593712,0.0002059166,0.0007062501,0.0005201249,0.0002820235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003385705,0.000446402,0.008322285,0.00143452,0.0005221228,0.0001554321,0.01798152,0.1645794,0.009444853,0.4038243,0.05962456,0.3302788],"study_design_scores_gemma":[0.002294131,0.00009902377,0.0002269244,0.00002350327,0.00001425315,0.00001329226,0.00001285875,0.7847655,0.008141444,0.2039222,0.0001667093,0.0003200732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01536729,0.00008988135,0.9814936,0.0008858999,0.000880337,0.0004758046,0.00001198444,0.0002332682,0.0005619509],"genre_scores_gemma":[0.942932,0.000006712189,0.05550109,0.001192309,0.0001372703,0.00009623309,0.000007072525,0.00001581,0.0001115451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9275647,"threshold_uncertainty_score":0.9356704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04771973624334411,"score_gpt":0.3189510584323415,"score_spread":0.2712313221889974,"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."}}