{"id":"W4407953359","doi":"10.1145/3701551.3703480","title":"Query Performance Prediction: Theory, Techniques and Applications","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Data mining","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":[0.004746571,0.001612261,0.002030057,0.003483178,0.0006239932,0.002792202,0.002422106,0.001823253,0.001898765],"category_scores_gemma":[0.01944849,0.0006819148,0.0009362485,0.007530059,0.00165857,0.003508885,0.001676258,0.002536928,0.001224046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166815,"about_ca_system_score_gemma":0.001260636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005093614,"about_ca_topic_score_gemma":0.002092077,"domain_scores_codex":[0.9957741,0.001139591,0.0002755836,0.0009632058,0.001590583,0.0002569343],"domain_scores_gemma":[0.9856939,0.00987165,0.001335378,0.001170416,0.001710201,0.0002184918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001977482,0.0003005065,0.0119802,0.001095779,0.0002398707,0.0002130579,0.0003157756,0.2700944,0.004376034,0.1098404,0.01738595,0.5839603],"study_design_scores_gemma":[0.00001677445,0.0001099738,0.002228497,0.000149269,0.0000714685,0.000229667,0.00006995015,0.8914254,0.001427639,0.09521624,0.008989203,0.00006605597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007132093,0.02997131,0.9557378,0.001474059,0.0002079002,0.000102022,0.0004119948,0.0008433989,0.004119524],"genre_scores_gemma":[0.5862728,0.05499191,0.3469816,0.0009971228,0.003791613,0.0005813302,0.00170634,0.0003164299,0.004360933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005093614,"threshold_uncertainty_score":0.02510256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004753132183198136,"score_gpt":0.2203244713222114,"score_spread":0.2155713391390133,"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."}}