{"id":"W2141385938","doi":"10.1109/icdar.1995.602070","title":"A Markovian random field approach to information retrieval","year":2002,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Markov process; Analogy; Markov chain; Theoretical computer science; Random field; Set (abstract data type); Matching (statistics); Field (mathematics); Information retrieval; Algorithm; Data mining; Artificial intelligence; Machine learning; Mathematics","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":[0.002760697,0.0008195782,0.001489593,0.003128154,0.0007881277,0.001808007,0.002572101,0.002626495,0.004973918],"category_scores_gemma":[0.007364131,0.0005350209,0.00152364,0.002890449,0.002162218,0.004348666,0.001138764,0.002110429,0.001272445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002260596,"about_ca_system_score_gemma":0.001549916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004111071,"about_ca_topic_score_gemma":0.002787065,"domain_scores_codex":[0.9981309,0.0007425164,0.0001081058,0.0003430407,0.0005439336,0.0001315022],"domain_scores_gemma":[0.9966614,0.002440075,0.0002037541,0.0002575717,0.0003496517,0.00008748689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006072472,0.0001109192,0.0005708013,0.0003613168,0.0001053393,0.0002278992,0.000186448,0.1641191,0.002199323,0.7304864,0.006614664,0.09495703],"study_design_scores_gemma":[0.00003430486,0.00011276,0.0003239411,0.00006197021,0.00004241121,0.0002130276,0.00002051635,0.5547135,0.0007593905,0.4319221,0.01172997,0.0000661439],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003553357,0.004490226,0.9851164,0.001460138,0.0001835628,0.0001547746,0.0001534692,0.0003085441,0.004579469],"genre_scores_gemma":[0.3691484,0.0125767,0.588789,0.001663607,0.002142511,0.001130743,0.0006257738,0.000216683,0.02370646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004973918,"threshold_uncertainty_score":0.01663941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361539952366508,"score_gpt":0.2069330651218659,"score_spread":0.1933176655982008,"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."}}