{"id":"W4403582486","doi":"10.1145/3627673.3679729","title":"No Query Left Behind: Query Refinement via Backtranslation","year":2024,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Query optimization; Computer science; Sargable; Query expansion; RDF query language; Web search query; Query language; Information retrieval; Query by Example; Web query classification; Database; Search engine","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":[0.008173641,0.002378184,0.001825051,0.002742351,0.001276914,0.002208184,0.003076045,0.001543532,0.002919647],"category_scores_gemma":[0.03813145,0.0007288269,0.00221907,0.003202806,0.001873027,0.006226896,0.004538347,0.002688024,0.003769682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170559,"about_ca_system_score_gemma":0.002401246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008777395,"about_ca_topic_score_gemma":0.009267142,"domain_scores_codex":[0.9838108,0.00709802,0.001758308,0.003516904,0.00313684,0.0006790492],"domain_scores_gemma":[0.9718677,0.01048823,0.001331957,0.0112711,0.004605656,0.0004353154],"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":[0.003928758,0.001850603,0.0235469,0.003280095,0.0006564918,0.001510074,0.007680776,0.0446399,0.1810869,0.01007981,0.071522,0.6502178],"study_design_scores_gemma":[0.001220602,0.003101386,0.01860969,0.000250722,0.0006211974,0.003516483,0.003546599,0.6297253,0.1838875,0.02784631,0.1269837,0.0006904458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.405568,0.003262091,0.4640448,0.002109784,0.0006131969,0.003446019,0.01436228,0.09523638,0.01135746],"genre_scores_gemma":[0.5030676,0.0005411684,0.4367123,0.00175493,0.0001946858,0.001278579,0.04711096,0.003666979,0.005672754],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008777395,"threshold_uncertainty_score":0.0432269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025811830274832,"score_gpt":0.24443556918386,"score_spread":0.2341774508811117,"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."}}