{"id":"W2073971205","doi":"10.5715/jnlp.7.2_117","title":"The Exploration and Analysis of Using Multiple Thesaurus Types for Query Expansion in Information Retrieval.","year":2000,"lang":"en","type":"article","venue":"Journal of Natural Language Processing","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; York University","keywords":"Thesaurus; Information retrieval; Query expansion; Computer science; Natural language processing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004536537,0.00004690725,0.0001389502,0.0001986447,0.00007938487,0.0001156018,0.0001531947,0.00002987615,6.313993e-7],"category_scores_gemma":[0.0002902063,0.00002724167,0.00004625464,0.0004733515,0.00002048309,0.001746273,0.00001564193,0.00008002204,6.347137e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001998016,"about_ca_system_score_gemma":0.00004321973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001755488,"about_ca_topic_score_gemma":0.00002805796,"domain_scores_codex":[0.9994,0.00002849307,0.0003003112,0.00004556661,0.0001473706,0.00007831249],"domain_scores_gemma":[0.9993079,0.0001845757,0.0002871261,0.00006493103,0.0001428714,0.00001259914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001410084,0.000009395417,0.001023126,0.00003292625,0.00003500334,0.000002481503,0.008465014,0.002128173,0.003348301,0.00004506623,0.000003232796,0.9847662],"study_design_scores_gemma":[0.0003244581,0.00003523249,0.008118277,0.00008900955,0.00006440552,0.00001462586,0.002093854,0.9853246,0.003574008,0.0002603047,0.00004608859,0.00005510151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9353224,0.009309198,0.05494,0.0002867505,0.00006551068,0.00005716801,5.337313e-7,0.000006497432,0.0000119439],"genre_scores_gemma":[0.980897,0.0001072173,0.01893175,0.00003585362,0.00001983928,3.015379e-7,9.094335e-7,0.000001296603,0.000005848379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9847112,"threshold_uncertainty_score":0.1266006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01703092278076369,"score_gpt":0.2835799331783141,"score_spread":0.2665490103975505,"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."}}