{"id":"W4312887229","doi":"10.14778/3551793.3551857","title":"Tiresias","year":2022,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Latency (audio); Search engine indexing; Adaptation (eye); Row; Parallel computing; Artificial intelligence; Database","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":[0.001787286,0.001736219,0.0008667429,0.001034716,0.0006780112,0.003369396,0.004133083,0.001148827,0.05083017],"category_scores_gemma":[0.007797119,0.0009858424,0.001117138,0.001103367,0.0005456153,0.004423195,0.002785026,0.003069475,0.0389627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009937737,"about_ca_system_score_gemma":0.002418685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005444543,"about_ca_topic_score_gemma":0.005951337,"domain_scores_codex":[0.9982065,0.0001966632,0.000116176,0.0005037574,0.0007538702,0.0002230702],"domain_scores_gemma":[0.9974382,0.000477762,0.0001807912,0.001047491,0.0006521423,0.0002035814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00169718,0.0004521676,0.00629654,0.0008756444,0.0002479779,0.0003171871,0.0003017093,0.02869581,0.01137573,0.03761979,0.5301471,0.3819732],"study_design_scores_gemma":[0.0002480665,0.0003686341,0.00236619,0.0001655593,0.0001136983,0.0006047152,0.0000950117,0.3442615,0.01855338,0.03190106,0.6011443,0.0001778187],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.01766047,0.002897812,0.3305187,0.002328319,0.00117874,0.0009075585,0.01984121,0.5220715,0.1025957],"genre_scores_gemma":[0.3130161,0.004209164,0.3924939,0.002891195,0.0006514529,0.001404174,0.1008649,0.03067664,0.1537925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05083017,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009267484417925695,"score_gpt":0.194806908339679,"score_spread":0.1855394239217533,"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."}}