{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002783793,0.000172641,0.0002395328,0.0001737432,0.00006174337,0.0005580151,0.0006675206,0.0001854564,0.00003384456],"category_scores_gemma":[0.00002762159,0.000148506,0.00004630406,0.000160417,0.00001655158,0.0005592899,0.001272955,0.0001650688,0.00002956302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002008092,"about_ca_system_score_gemma":0.0001092811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001916267,"about_ca_topic_score_gemma":0.000006906182,"domain_scores_codex":[0.9986492,0.00008585113,0.0002208593,0.0006945134,0.0001997009,0.0001498817],"domain_scores_gemma":[0.9985265,0.00003199376,0.0001170301,0.001183284,0.00007706103,0.00006414302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002720891,0.00006789546,0.0006118416,0.00008002807,0.00005901984,0.000008415662,0.0004275078,0.964236,0.0001845659,0.001472455,0.004475461,0.02837412],"study_design_scores_gemma":[0.00008205931,0.00002313232,0.0005303717,0.00005163426,0.00001834345,0.000003281114,0.00002030461,0.9987413,0.000103045,0.00009117865,0.0001185846,0.0002167515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03154377,0.00006983579,0.9659779,0.0002526929,0.000288723,0.0001040336,0.00001032656,0.001170733,0.0005820066],"genre_scores_gemma":[0.3160859,0.0001323614,0.6818173,0.0002919178,0.0000697496,0.00001065563,0.0004440937,0.00001624229,0.001131763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2845422,"threshold_uncertainty_score":0.6055901,"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."}}