{"id":"W2971120804","doi":"10.14778/3352063.3352064","title":"GALO","year":2019,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; IBM (Canada)","funders":"","keywords":"Computer science; SPARQL; Knowledge base; SQL; Query optimization; Process (computing); Query plan; Plan (archaeology); Information retrieval; Base (topology); RDF; Web query classification; Sargable; Database; Data mining; Web search query; World Wide Web; Search engine; Semantic Web; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0001492797,0.00008163986,0.000118469,0.00002920426,0.00004519318,0.00002287337,0.0006022268,0.00001704354,0.00001507445],"category_scores_gemma":[0.00002068317,0.00004999988,0.00005848602,0.0001843103,0.00002466989,0.0004454415,0.0004962267,0.00005890976,0.0000528217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002567573,"about_ca_system_score_gemma":0.00001454908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001959135,"about_ca_topic_score_gemma":5.734331e-7,"domain_scores_codex":[0.9992317,0.000002290614,0.000159921,0.0001946891,0.0002557566,0.0001556213],"domain_scores_gemma":[0.9995089,0.00001314812,0.000142524,0.0002198895,0.0000846883,0.00003083019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003901369,0.0000263352,0.004848587,0.00007756338,0.00001172441,1.253925e-7,0.0003667179,0.00000868696,0.0638581,0.9268997,0.00190935,0.001989212],"study_design_scores_gemma":[0.0008364575,0.0001776493,0.006512372,0.0003000527,0.00001044713,0.00003765729,0.0004212268,0.00143875,0.6570215,0.010726,0.3221568,0.0003610974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.790643,0.0006672369,0.03792579,0.004586798,0.003205821,0.002140955,0.00002351572,0.0003934808,0.1604134],"genre_scores_gemma":[0.972675,0.00001203102,0.02501776,0.0001551574,0.00003865726,0.00002077725,2.515663e-7,0.000006192799,0.002074224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9161737,"threshold_uncertainty_score":0.2038936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00448911751621578,"score_gpt":0.1879691187956926,"score_spread":0.1834800012794768,"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."}}