{"id":"W2737301218","doi":"","title":"Query Expansion Using Pseudo Relevance Feedback on Wikipedia","year":2016,"lang":"en","type":"article","venue":"Web Search and Data Mining","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; Athabasca University","funders":"","keywords":"Relevance feedback; Computer science; Query expansion; Relevance (law); Information retrieval; World Wide Web; Artificial intelligence; Political 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005780669,0.000102566,0.0001115492,0.00008459824,0.000137122,0.000128902,0.0008895583,0.00004390431,0.000009587689],"category_scores_gemma":[0.0002049307,0.00006574064,0.00001184517,0.0001269707,0.00004760261,0.00106538,0.001174,0.00009839252,0.00003550751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002686053,"about_ca_system_score_gemma":0.0001352679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000340662,"about_ca_topic_score_gemma":0.00001322179,"domain_scores_codex":[0.9984903,0.00006608589,0.0001559284,0.0006401531,0.0003034789,0.0003440636],"domain_scores_gemma":[0.9982723,0.0002404092,0.00002964043,0.00128887,0.00003306977,0.0001356969],"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":[0.00002283996,0.00002322485,0.002168586,0.00002552543,0.000007843263,0.0000418809,0.0005896988,0.00006370799,0.04123805,0.002633432,0.001272758,0.9519125],"study_design_scores_gemma":[0.0006697453,0.00009578542,0.0008699967,0.0004485446,0.000003794095,0.00003713621,0.0001515726,0.9912048,0.001490767,0.0002804908,0.004474454,0.0002729181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6672054,0.000124355,0.3310574,0.0009808274,0.0001854235,0.00006368051,0.00002121746,0.00007004136,0.0002916596],"genre_scores_gemma":[0.8067055,0.0001589847,0.1924906,0.0002136049,0.0002030396,0.000001329183,0.000005580329,0.000009932575,0.0002114158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9911411,"threshold_uncertainty_score":0.2680826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1144135644946151,"score_gpt":0.3287476804346895,"score_spread":0.2143341159400745,"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."}}