{"id":"W2189229433","doi":"","title":"York University at TREC 2011: Medical Records Track","year":2011,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Query expansion; Information retrieval; Unified Medical Language System; Search engine indexing; Ranking (information retrieval); Matching (statistics); Relevance feedback; Relevance (law); Weighting; Semantic matching; Web search query; Query language; Image retrieval; Search engine; Artificial intelligence; Medicine","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":[0.017588,0.002401253,0.00281536,0.006781226,0.00425907,0.005622849,0.003329585,0.003725795,0.05984994],"category_scores_gemma":[0.03250564,0.0008975348,0.001173161,0.007602286,0.001126811,0.007954733,0.00218322,0.004540936,0.03974666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009194215,"about_ca_system_score_gemma":0.0143153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1387676,"about_ca_topic_score_gemma":0.2070098,"domain_scores_codex":[0.9900224,0.003178596,0.0007945645,0.0008592062,0.004422344,0.0007230413],"domain_scores_gemma":[0.9628198,0.009257139,0.001712929,0.00460683,0.01813819,0.003465017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001063849,0.0001067088,0.0003263286,0.0002626386,0.00002735069,0.00003035824,0.00003590966,0.0004129502,0.0006627701,0.0007656173,0.9866757,0.01058743],"study_design_scores_gemma":[0.0007480828,0.0004558676,0.01099573,0.0004151652,0.0001854303,0.0003183724,0.0003299868,0.01591117,0.006323713,0.004788362,0.9592635,0.0002645228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0196343,0.01443957,0.01821531,0.06573671,0.008030111,0.003565812,0.7451655,0.02553679,0.09967584],"genre_scores_gemma":[0.02301967,0.004400619,0.01856805,0.005049051,0.001288811,0.00166203,0.883591,0.001197694,0.061223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1387676,"threshold_uncertainty_score":0.2759199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03467695990857757,"score_gpt":0.2325296340621002,"score_spread":0.1978526741535226,"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."}}