{"id":"W2956462957","doi":"10.1109/compsac.2019.00070","title":"Xu: An Automated Query Expansion and Optimization Tool","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Gnowit (Canada); Queen's University","funders":"","keywords":"Computer science; Query expansion; Information retrieval; Scalability; Query optimization; Set (abstract data type); Precision and recall; Noise (video); Search engine; Similarity (geometry); Data mining; Semantic similarity; Artificial intelligence; Database","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":[0.002096534,0.002058742,0.001465486,0.002793982,0.0005589573,0.001395534,0.001984756,0.0009592052,0.008766429],"category_scores_gemma":[0.008769255,0.000802885,0.001442322,0.002452485,0.0005098534,0.00263299,0.002152434,0.001154937,0.004001543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005440058,"about_ca_system_score_gemma":0.00107834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002954948,"about_ca_topic_score_gemma":0.002831982,"domain_scores_codex":[0.997693,0.0006355966,0.0003015836,0.0004723366,0.000743145,0.0001542614],"domain_scores_gemma":[0.9974875,0.001576921,0.0001695995,0.00028709,0.0004099976,0.00006884334],"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":[0.001854798,0.0003262808,0.00442575,0.001839028,0.0003583587,0.0006172487,0.0007642966,0.02100188,0.05975542,0.009888528,0.1185706,0.7805979],"study_design_scores_gemma":[0.0005613723,0.0008058852,0.003815789,0.00011319,0.0001402263,0.001054203,0.0004706006,0.8113702,0.09592757,0.01464071,0.07087789,0.0002223276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01073812,0.0007027597,0.7447464,0.0002115592,0.00006068063,0.0004150213,0.003223359,0.2381646,0.001737493],"genre_scores_gemma":[0.1150363,0.000495765,0.860975,0.0004411143,0.00007881032,0.0009517928,0.01036794,0.0077701,0.003883218],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.008766429,"threshold_uncertainty_score":0.02932668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0181041184139406,"score_gpt":0.2720603972268492,"score_spread":0.2539562788129086,"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."}}