{"id":"W4406734274","doi":"10.4230/lipics.icdt.2025.15","title":"Query Repairs","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Deutsche Forschungsgemeinschaft; National Science Foundation","keywords":"Computer science; Information retrieval","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":[0.01596083,0.001701423,0.002876427,0.002126608,0.002012209,0.004018665,0.006182724,0.003635814,0.006989368],"category_scores_gemma":[0.09080753,0.001121105,0.002219974,0.002206972,0.00341832,0.009608839,0.006002315,0.004546094,0.001956386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002312029,"about_ca_system_score_gemma":0.003069142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005003965,"about_ca_topic_score_gemma":0.003117837,"domain_scores_codex":[0.9720992,0.007823709,0.002454043,0.005896566,0.01003227,0.00169419],"domain_scores_gemma":[0.8919139,0.06506044,0.006842258,0.02321942,0.01095505,0.002008942],"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":[0.002964044,0.001029338,0.01363784,0.002987622,0.0005433594,0.002073996,0.006585414,0.1911769,0.03893296,0.190337,0.02830313,0.5214283],"study_design_scores_gemma":[0.0001860246,0.001011461,0.002622017,0.0002863533,0.0003458125,0.002302713,0.00243382,0.6917108,0.04043806,0.2211172,0.03736687,0.000178757],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05322716,0.001504889,0.9331295,0.002124013,0.000209698,0.0005583478,0.0009670064,0.00426125,0.0040181],"genre_scores_gemma":[0.5695687,0.0007209373,0.4166091,0.001032099,0.00034524,0.0004684183,0.002311786,0.001022526,0.007921339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01596083,"threshold_uncertainty_score":0.08440995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03890775988914937,"score_gpt":0.2869774644331143,"score_spread":0.2480697045439649,"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."}}