{"id":"W2751154306","doi":"10.5555/3135595.3135623","title":"Distribution testing lower bounds via reductions from communication complexity","year":2017,"lang":"en","type":"article","venue":"Apollo (University of Cambridge)","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical proof; Simple (philosophy); Mathematics; Measure (data warehouse); Reduction (mathematics); Upper and lower bounds; Distribution (mathematics); Connection (principal bundle); Discrete mathematics; Property testing; Sample (material); Computer science; Mathematical analysis; Data mining","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002023358,0.0001098788,0.0001839116,0.00005506548,0.002275983,0.0001595207,0.002069855,0.000063907,0.00002088292],"category_scores_gemma":[0.00007760427,0.0001484685,0.0001128677,0.0002059345,0.0007806228,0.001103827,0.0009159176,0.0001830753,0.0000280224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007128945,"about_ca_system_score_gemma":0.00006001364,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01201778,"about_ca_topic_score_gemma":0.0003982558,"domain_scores_codex":[0.9991038,0.00007199005,0.0001240999,0.0002891887,0.0002216932,0.0001892057],"domain_scores_gemma":[0.9976471,0.0001084981,0.0003153539,0.00163684,0.000204428,0.00008777642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000886061,0.0008887232,0.01800991,0.00004501489,0.0003032212,0.00005697567,0.00208554,0.00007892466,0.004624989,0.7365986,0.02188086,0.2153386],"study_design_scores_gemma":[0.001009401,0.0001164608,0.8315039,0.0001124564,0.00006046052,0.00002190006,0.0003853206,0.1064666,0.0004790698,0.04280122,0.01657459,0.0004686484],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2971621,0.00006274656,0.6927871,0.0030195,0.0004478548,0.0001149625,0.0001574853,0.0001424637,0.006105768],"genre_scores_gemma":[0.9198744,0.00001669555,0.07962047,0.00002032383,0.00003967208,2.046586e-7,0.00007800389,0.00000377844,0.0003464884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.813494,"threshold_uncertainty_score":0.9990229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0477911743995175,"score_gpt":0.2392916382976616,"score_spread":0.1915004638981441,"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."}}