{"id":"W4392800119","doi":"10.1007/978-3-031-56066-8_6","title":"Estimating Query Performance Through Rich Contextualized Query Representations","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo; University of Guelph","funders":"","keywords":"Computer science; Query optimization; Sargable; Query expansion; Query language; Information retrieval; Web search query; Query by Example; RDF query language; Web query classification; 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":[0.001507205,0.001051343,0.001471096,0.001600537,0.0003080158,0.00185282,0.0009374665,0.001150803,0.001017422],"category_scores_gemma":[0.008389334,0.000472703,0.0006353783,0.002222697,0.0004699041,0.002844731,0.0008809033,0.00116779,0.0008364632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006763197,"about_ca_system_score_gemma":0.0007576531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003543581,"about_ca_topic_score_gemma":0.004210273,"domain_scores_codex":[0.9981467,0.0004587735,0.000104081,0.0004072175,0.0006766233,0.000206748],"domain_scores_gemma":[0.996453,0.002265366,0.0002838291,0.0004760173,0.0004397638,0.00008199952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002669637,0.0008061365,0.01690781,0.0003733779,0.0003745018,0.0002322304,0.000187089,0.3568824,0.09464466,0.00462152,0.009801459,0.5124993],"study_design_scores_gemma":[0.00001835182,0.0002052371,0.002647005,0.000007598762,0.00005666074,0.0001025881,0.00002974071,0.9860469,0.007882692,0.002357596,0.000623715,0.00002191448],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3118572,0.004091743,0.6733056,0.0004616499,0.0001235921,0.0001324927,0.002118167,0.005852184,0.002057417],"genre_scores_gemma":[0.8502907,0.001060187,0.1428206,0.0001333757,0.0002642356,0.00008806682,0.003990263,0.0002962209,0.001056404],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003543581,"threshold_uncertainty_score":0.007970989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02594049667470165,"score_gpt":0.2949049878235664,"score_spread":0.2689644911488648,"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."}}