{"id":"W2139061136","doi":"10.1007/11863878_56","title":"MedSearch: A Retrieval System for Medical Information Based on Semantic Similarity","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Information retrieval; Similarity (geometry); National library; Computer science; Semantic similarity; Medical information; World Wide Web; Artificial intelligence; Library science","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":[0.001262612,0.001129935,0.001677419,0.007118975,0.00046946,0.001471593,0.001022617,0.00138653,0.02012344],"category_scores_gemma":[0.003896798,0.0004733746,0.0008887135,0.00379044,0.0003400429,0.003017965,0.002042048,0.000588173,0.009971352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005033488,"about_ca_system_score_gemma":0.0007042802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009985123,"about_ca_topic_score_gemma":0.00149691,"domain_scores_codex":[0.9994455,0.0001212239,0.00008541521,0.00008423383,0.000224999,0.00003867036],"domain_scores_gemma":[0.9990239,0.0004738656,0.0001133905,0.00009915461,0.0001822357,0.0001074516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001851115,0.0003090464,0.003181271,0.002429612,0.0004916561,0.0005908355,0.0004051477,0.002350445,0.04638469,0.007212909,0.2192475,0.7155458],"study_design_scores_gemma":[0.002512728,0.002115538,0.01952568,0.0008427693,0.002520529,0.011908,0.0009758857,0.1794783,0.1259828,0.05629735,0.5972515,0.0005889726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06086662,0.02131775,0.7322534,0.002417973,0.001095746,0.001507347,0.03226345,0.123641,0.02463673],"genre_scores_gemma":[0.164649,0.008270001,0.7338249,0.002231741,0.001464948,0.001298075,0.05795501,0.004479198,0.02582712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02012344,"threshold_uncertainty_score":0.06731963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02505028022403974,"score_gpt":0.2595427266373837,"score_spread":0.234492446413344,"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."}}