{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002894092,0.0003050643,0.0004046774,0.0001358087,0.000137805,0.00005561287,0.001055454,0.0002085423,0.00001745254],"category_scores_gemma":[0.0001314573,0.0002862978,0.0001858292,0.0002365342,0.00006265916,0.0003749556,0.003888141,0.0005471969,0.000177858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008191776,"about_ca_system_score_gemma":0.0003311264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003183025,"about_ca_topic_score_gemma":0.00005902833,"domain_scores_codex":[0.9979553,0.00009319503,0.0004237895,0.0009574521,0.0002352841,0.0003350136],"domain_scores_gemma":[0.9970285,0.00009592852,0.0002243538,0.002435291,0.000119752,0.00009616103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002887184,0.0002417759,0.2659883,0.002091355,0.0004048371,0.0005412388,0.002285456,0.001861239,0.0005069118,0.6403276,0.05593341,0.02978899],"study_design_scores_gemma":[0.0003046775,0.00004616437,0.03701323,0.001275748,0.00003121841,0.00002223027,0.0001002866,0.003356781,0.002397391,0.004152237,0.9501815,0.001118528],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05612742,0.001443687,0.9221086,0.001146368,0.004951925,0.0004181626,0.0001197477,0.001033994,0.01265008],"genre_scores_gemma":[0.4712697,0.001064165,0.4603088,0.004066466,0.0020232,0.0006449302,0.0003694051,0.00008859635,0.06016463],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8942481,"threshold_uncertainty_score":0.9999589,"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."}}