{"id":"W4368755068","doi":"10.4230/lipics.approx/random.2023.4","title":"Experimental Design for Any $p$-Norm","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fuzhou University; Natural Science Foundation of Fujian Province","keywords":"Norm (philosophy); Mathematical optimization; Mathematics; Interpolation (computer graphics); Computer science; Algorithm; Applied mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008146394,0.0002194865,0.0001869114,0.0001413025,0.00007494537,0.00004533363,0.0002866448,0.0002127521,0.0000468529],"category_scores_gemma":[0.000008450522,0.0002748099,0.00010855,0.0001125581,0.00002189661,0.00009450725,0.0001635605,0.0001793232,0.00004661442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001552585,"about_ca_system_score_gemma":0.00002820947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001813256,"about_ca_topic_score_gemma":0.000003701133,"domain_scores_codex":[0.999222,0.00001219959,0.000110741,0.000385794,0.00003478564,0.0002344213],"domain_scores_gemma":[0.9995112,0.00005017145,0.00004486835,0.0002826275,0.00003625092,0.00007487356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002102103,0.00001386937,0.00002793698,0.000198652,0.00005434895,0.00001471363,0.0001046727,0.9971555,0.00005400916,0.001226908,0.001076169,0.00005215193],"study_design_scores_gemma":[0.0003371245,0.00002841153,0.00008153686,0.00005683214,0.00004539488,4.353471e-7,0.00008320536,0.9804145,0.01417917,0.003855163,0.0005632223,0.0003549913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05698974,0.0001010694,0.9400431,0.000007939968,0.0007619885,0.000449666,0.00003055529,0.0008793374,0.0007366148],"genre_scores_gemma":[0.9949212,0.0001399628,0.002581249,0.000008795136,0.00009272935,0.000008175904,0.00006277046,0.00006745596,0.002117641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9379315,"threshold_uncertainty_score":0.9999704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1123993981993143,"score_gpt":0.1900509867269578,"score_spread":0.07765158852764358,"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."}}