{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008368365,0.0006309839,0.0009147078,0.006565724,0.000932851,0.001949714,0.001351113,0.0008385753,0.001476574],"category_scores_gemma":[0.02976194,0.0006723528,0.001287069,0.004403377,0.0009591386,0.00648105,0.001508527,0.0007212284,0.000678918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008263371,"about_ca_system_score_gemma":0.001496693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002323196,"about_ca_topic_score_gemma":0.00264214,"domain_scores_codex":[0.9921754,0.004461875,0.000783129,0.0005837692,0.001857261,0.0001385059],"domain_scores_gemma":[0.9799457,0.01427061,0.001161651,0.00189064,0.00244263,0.0002887046],"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":[0.0009170882,0.0005097411,0.01013933,0.002651305,0.0005293553,0.0006782254,0.003083069,0.01099293,0.0512015,0.02337325,0.005774187,0.8901501],"study_design_scores_gemma":[0.0004418173,0.002408353,0.04311363,0.00191565,0.002418895,0.01278939,0.005792365,0.5754301,0.14883,0.07674491,0.1292079,0.0009069553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.152803,0.01031087,0.8228758,0.001279276,0.0001455209,0.00155311,0.0007847701,0.001782144,0.008465605],"genre_scores_gemma":[0.2624173,0.002003517,0.731969,0.0001951193,0.00007939637,0.0005589662,0.0008779292,0.0001896818,0.001709078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008368365,"threshold_uncertainty_score":0.04425663,"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."}}