{"id":"W2979662338","doi":"10.4230/lipics.stacs.2020.40","title":"Succinct Population Protocols for Presburger Arithmetic","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Presburger arithmetic; Computer science; Arithmetic; Population; Mathematics; Theoretical computer science; Medicine; Decidability","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001917481,0.0002313903,0.0002458112,0.00005198134,0.00007765448,0.00003533193,0.0003845,0.000480195,0.00001510252],"category_scores_gemma":[0.00008115464,0.0002201433,0.0002610139,0.00007330908,0.00003581728,0.000003346675,0.0006177755,0.0001829442,0.00001219822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003531726,"about_ca_system_score_gemma":0.00005990402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003498591,"about_ca_topic_score_gemma":0.000009125613,"domain_scores_codex":[0.9986484,0.00007853313,0.0001654387,0.0008079361,0.00003648279,0.0002632068],"domain_scores_gemma":[0.9990738,0.00003133854,0.0001873785,0.0005247533,0.0001142705,0.00006847817],"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":[0.002574721,0.0008199162,0.4196658,0.001908022,0.0008504186,0.00006182223,0.00004293901,0.4888892,0.05738014,0.01461971,0.00672752,0.006459725],"study_design_scores_gemma":[0.01061218,0.005426182,0.2261711,0.001334154,0.0009549739,0.00002592785,0.0001498511,0.4363662,0.04872344,0.07427821,0.1888617,0.007096169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359001,0.00003728288,0.05544566,0.0000256807,0.0002460129,0.007472746,0.00004701129,0.00004175514,0.0007837604],"genre_scores_gemma":[0.9960213,0.00001256491,0.0003936584,0.0000800933,0.0002556189,0.00007364556,0.0005122466,0.00001993694,0.00263098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1934947,"threshold_uncertainty_score":0.8977184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08722865809221507,"score_gpt":0.2292911583890881,"score_spread":0.142062500296873,"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."}}