{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001896636,0.0009965023,0.001478178,0.003595929,0.0008029473,0.001105967,0.001068622,0.0009844506,0.003491226],"category_scores_gemma":[0.01244132,0.0004229163,0.000830568,0.002221624,0.0003661195,0.002601621,0.001011294,0.0007988997,0.001574088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000630534,"about_ca_system_score_gemma":0.001473911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006042053,"about_ca_topic_score_gemma":0.01127486,"domain_scores_codex":[0.9975029,0.00103347,0.0001730477,0.0003592552,0.0007623814,0.0001691053],"domain_scores_gemma":[0.993141,0.004086787,0.0001726529,0.0005249623,0.00189652,0.0001781165],"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":[0.002657194,0.001125149,0.005484166,0.001289131,0.0002787055,0.0007241332,0.0005645712,0.07117137,0.06716685,0.007850572,0.05073577,0.7909523],"study_design_scores_gemma":[0.0001047905,0.0002595614,0.001583658,0.00003400172,0.0001341972,0.0002901358,0.0001062663,0.9738931,0.0132986,0.005994038,0.004256326,0.00004532583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2670388,0.005950614,0.6995599,0.00164233,0.0009382787,0.0007968302,0.003033212,0.0113144,0.009725607],"genre_scores_gemma":[0.7706115,0.0008724137,0.2153701,0.0003074849,0.0005363548,0.0003218462,0.004009333,0.0004406119,0.007530477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006042053,"threshold_uncertainty_score":0.01201379,"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."}}