{"id":"W4409166166","doi":"10.1007/978-3-031-88720-8_38","title":"Advancing Query Performance Prediction: Challenges and Adaptive Solutions","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Query optimization; Information retrieval; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004220892,0.0002998419,0.0004220017,0.0009689768,0.0004466028,0.0003468791,0.001439029,0.0001759884,0.00004255391],"category_scores_gemma":[0.000585557,0.0002474501,0.00006752128,0.0003976388,0.0008722403,0.0009381984,0.001900661,0.000491255,0.00003528193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001651651,"about_ca_system_score_gemma":0.0003059555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001078086,"about_ca_topic_score_gemma":0.0003148217,"domain_scores_codex":[0.9960993,0.00006297185,0.000598532,0.001358261,0.001423505,0.0004574076],"domain_scores_gemma":[0.9969599,0.001378765,0.0002353573,0.001030564,0.0002802855,0.0001150756],"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":[0.000007662416,0.00001129846,0.00001548004,0.00002803231,0.000008041846,0.000006947248,0.0005490949,0.005216054,0.000001648361,0.04007463,0.0003306271,0.9537505],"study_design_scores_gemma":[0.0003288506,0.0003746541,0.002324926,0.001214537,0.00003139963,0.0000309499,0.00002056789,0.346992,0.00005002898,0.5406549,0.1073006,0.0006765569],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00003848532,0.004606972,0.9575499,0.002534507,0.00166374,0.0003105782,0.0000438223,0.00006471702,0.03318733],"genre_scores_gemma":[0.4889973,0.03407869,0.4295983,0.01086639,0.003229485,0.0001158487,0.00006104847,0.0001032766,0.03294967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9530739,"threshold_uncertainty_score":0.9999978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014172358647634,"score_gpt":0.3252785790447352,"score_spread":0.2238613431799719,"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."}}