{"id":"W2766617320","doi":"10.4310/cms.2017.v15.n7.a2","title":"Discrete-in-time random particle blob method for the Keller–Segel equation and convergence analysis","year":2017,"lang":"en","type":"article","venue":"Communications in Mathematical Sciences","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Convergence (economics); Mathematics; Applied mathematics; Discrete time and continuous time; Particle (ecology); Particle system; Mathematical analysis; Statistical physics; Physics; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007945444,0.0001561504,0.0004925609,0.0001088248,0.001060587,0.0002497458,0.002740753,0.0000790367,0.0001501534],"category_scores_gemma":[0.01071386,0.00009610734,0.0001241548,0.0005143452,0.001860953,0.0002318165,0.0007326984,0.0001797268,0.00002712991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003101397,"about_ca_system_score_gemma":0.00003261517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004396487,"about_ca_topic_score_gemma":0.0002424727,"domain_scores_codex":[0.9979934,0.00039573,0.0006642935,0.0003192594,0.0002903668,0.0003369711],"domain_scores_gemma":[0.9800953,0.0175167,0.0003039358,0.001956288,0.00005784246,0.00006988736],"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":[0.00001853559,0.0002539235,0.01399756,0.00006842368,0.00007391239,4.402771e-7,0.001186127,0.00002962001,0.0002243276,0.9831402,0.00004012958,0.0009668309],"study_design_scores_gemma":[0.000369017,0.00001932871,0.003727723,0.00004037726,0.0001280538,0.000001758923,0.0002327417,0.4432212,0.0001624448,0.5519896,0.00001729715,0.00009038224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2336695,0.000523301,0.7336224,0.02141768,0.00005219594,0.002363364,0.00002089383,0.00007906669,0.00825162],"genre_scores_gemma":[0.7695406,0.0000807463,0.2298229,0.00004812659,0.000008447603,0.0003191541,0.000001563099,0.000006988322,0.000171487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5358711,"threshold_uncertainty_score":0.9976193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1897198442882994,"score_gpt":0.4600004709438622,"score_spread":0.2702806266555629,"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."}}