{"id":"W4223421616","doi":"10.1016/j.aim.2023.109004","title":"The ASEP speed process","year":2023,"lang":"en","type":"article","venue":"Advances in Mathematics","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley; Centre de Recherches Mathématiques; Institute for Advanced Study; National Science Foundation","keywords":"Conjecture; Asymmetric simple exclusion process; Class (philosophy); Process (computing); Mathematics; Construct (python library); Infinity; Speedup; Combinatorics; Computer science; Parallel computing; Mathematical analysis; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001343129,0.0007434594,0.001090427,0.001651223,0.001246545,0.002820138,0.001325435,0.002793625,0.02709624],"category_scores_gemma":[0.01240685,0.0005473251,0.0008004626,0.00122497,0.002510895,0.005720635,0.002160733,0.003557908,0.002991477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159963,"about_ca_system_score_gemma":0.0008954685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002106267,"about_ca_topic_score_gemma":0.0007884514,"domain_scores_codex":[0.9992362,0.0002091872,0.00002421599,0.0001821577,0.0002194632,0.0001288432],"domain_scores_gemma":[0.9959596,0.001973903,0.0004490761,0.0003981955,0.0007785491,0.0004407827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002910367,0.00001351252,0.0002205889,0.00002389008,0.000007848358,0.00004942635,0.00005147886,0.007935584,0.0004954629,0.9831405,0.003017129,0.005015422],"study_design_scores_gemma":[0.00003407301,0.00003605061,0.0006887556,0.00002990443,0.00001488275,0.0002289314,0.00009154832,0.1509876,0.000543748,0.8385195,0.008787097,0.00003791977],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2027319,0.002322696,0.5787035,0.01385747,0.001243953,0.0001653154,0.0009626172,0.0007107717,0.1993019],"genre_scores_gemma":[0.8744041,0.001830194,0.01756097,0.0007447781,0.0009542783,0.0001582354,0.0003428053,0.0002196832,0.1037849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02709624,"threshold_uncertainty_score":0.09064597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03332071681981426,"score_gpt":0.3885042227047872,"score_spread":0.3551835058849729,"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."}}