{"id":"W4414596481","doi":"10.1145/3762197","title":"Query Performance Prediction Using Neural Query Space Proximity","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Guelph; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Query expansion; Embedding; Subspace topology; Query optimization; Query language; Sargable; Property (philosophy); Quality (philosophy); Ranking (information retrieval)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001761511,0.0001930435,0.0002394723,0.0007021362,0.000333004,0.00009291298,0.0005487319,0.0002405273,0.000002643383],"category_scores_gemma":[0.00004217871,0.0001770454,0.00005041631,0.001089053,0.0001340839,0.0006608528,0.00004898554,0.0004056393,0.000004523905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001135488,"about_ca_system_score_gemma":0.00005387304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006572487,"about_ca_topic_score_gemma":0.000004456145,"domain_scores_codex":[0.9987439,0.00004128206,0.0003418994,0.0004624511,0.000135294,0.00027516],"domain_scores_gemma":[0.9988223,0.0000704549,0.00009200812,0.0008377458,0.0001340581,0.00004345172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006017651,0.000307915,0.004256772,0.0004152371,0.0001451123,0.00002017186,0.0001577745,0.003446295,0.007090289,0.06905399,0.0001425779,0.9149037],"study_design_scores_gemma":[0.0004715767,0.0009665751,0.0004554142,0.0008968912,0.00007603393,0.000393661,0.0004700271,0.4496602,0.5062912,0.01771636,0.02195608,0.0006459468],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04718562,0.0009048388,0.9487718,0.0009349297,0.0006983221,0.0004485265,0.000005046594,0.0009120619,0.00013883],"genre_scores_gemma":[0.9818667,0.0009343722,0.01647854,0.00006189214,0.00001933707,0.00008718375,7.974485e-7,0.00001013328,0.0005410878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9346811,"threshold_uncertainty_score":0.7219703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02292029793186801,"score_gpt":0.2815320931837497,"score_spread":0.2586117952518817,"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."}}