{"id":"W2068977490","doi":"10.1038/npre.2009.3673.1","title":"GillespieSSA: A user-friendly stochastic simulation package for R","year":2009,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Stochastic processes and statistical mechanics","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Graphics; Set (abstract data type); R package; Population; Theoretical computer science; Population model; Simple (philosophy); Metapopulation; Stochastic modelling; Algorithm; Applied mathematics; Computational science; Programming language; Mathematics; Computer graphics (images); Statistics","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":[0.003601039,0.002261328,0.002604584,0.002109611,0.0006906142,0.00190012,0.00551941,0.001796283,0.1356468],"category_scores_gemma":[0.01703652,0.001924508,0.002872403,0.001956671,0.0006557648,0.00198105,0.003271438,0.003818863,0.06735731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009767383,"about_ca_system_score_gemma":0.002923084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006933285,"about_ca_topic_score_gemma":0.006086193,"domain_scores_codex":[0.9982285,0.0007129898,0.000151297,0.0003072168,0.0004635453,0.0001365466],"domain_scores_gemma":[0.9938874,0.003789323,0.0004553662,0.0007791679,0.0008462563,0.0002425169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002822047,0.00007314757,0.00212863,0.002556691,0.001131751,0.0004730748,0.0002966459,0.07308634,0.003847458,0.06157943,0.7667747,0.08777011],"study_design_scores_gemma":[0.0004720963,0.00008096589,0.001997999,0.0003574069,0.0003266687,0.0004643584,0.00005868492,0.2844226,0.005292325,0.1021981,0.6040315,0.0002971732],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001936894,0.0006232588,0.7865702,0.0007479381,0.0003969204,0.0002079364,0.04554803,0.155473,0.008495803],"genre_scores_gemma":[0.03854853,0.001000436,0.7417421,0.001011077,0.0002468275,0.00293697,0.04093556,0.1560602,0.0175183],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1356468,"threshold_uncertainty_score":0.4537839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03590081923318401,"score_gpt":0.3746122381313135,"score_spread":0.3387114188981295,"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."}}