{"id":"W4393074478","doi":"10.1007/978-3-031-56069-9_51","title":"Query Performance Prediction: From Fundamentals to Advanced Techniques","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004810833,0.000612573,0.0005799403,0.0007691632,0.0002657201,0.0004551467,0.00197444,0.0002443034,0.00003484757],"category_scores_gemma":[0.00003265391,0.0005450309,0.0001163934,0.0007033999,0.0003824833,0.001651992,0.002163348,0.0007250712,0.0002068565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004789313,"about_ca_system_score_gemma":0.0003316718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004070069,"about_ca_topic_score_gemma":0.00006582763,"domain_scores_codex":[0.9957165,0.00001659603,0.0006550944,0.002014853,0.0009737081,0.0006232535],"domain_scores_gemma":[0.9974245,0.0002025017,0.0001892878,0.001774713,0.0001694078,0.000239571],"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":[0.00001209744,0.00001701199,0.00004521017,0.00009929898,0.00001808065,0.0001047893,0.0007303896,0.005084019,0.001471521,0.04012092,0.0001030318,0.9521936],"study_design_scores_gemma":[0.0004375426,0.001297867,0.0002767885,0.00915714,0.00003234417,0.0002397279,0.00000247049,0.1506792,0.07668956,0.1467215,0.6113473,0.003118553],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002408181,0.001121926,0.9887206,0.0005713737,0.003630299,0.0006672578,0.0001649309,0.0007173547,0.004165468],"genre_scores_gemma":[0.01713693,0.0001958708,0.9775538,0.001605541,0.001143477,0.00008333126,0.00003940107,0.0000633807,0.002178279],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9490751,"threshold_uncertainty_score":0.9997001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01214727380455557,"score_gpt":0.2460426334651926,"score_spread":0.233895359660637,"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."}}