{"id":"W4221161403","doi":"10.30757/alea.v20-52","title":"Scaling limit of the collision measures of multiple random walks","year":2023,"lang":"en","type":"article","venue":"Latin American Journal of Probability and Mathematical Statistics","topic":"Stochastic processes and statistical mechanics","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Division of Mathematical Sciences; Labex Bézout; Agence Nationale de la Recherche","keywords":"Random walk; Mathematics; Scaling; Measure (data warehouse); Limit (mathematics); Scaling limit; Combinatorics; Partition function (quantum field theory); Event (particle physics); Discrete mathematics; Statistical physics; Sequence (biology); Partition (number theory); Function (biology); Collision; Physics; Mathematical analysis; Quantum mechanics; Statistics; Computer science; Geometry","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":[0.001801109,0.000687454,0.001179132,0.002312773,0.001315238,0.00225086,0.001240714,0.001110409,0.002291532],"category_scores_gemma":[0.01160565,0.0007764354,0.0009089703,0.0008053205,0.003020645,0.002989298,0.002034085,0.00148553,0.0003148888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001473972,"about_ca_system_score_gemma":0.0007984567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048165,"about_ca_topic_score_gemma":0.0005594677,"domain_scores_codex":[0.9988933,0.000225066,0.00006219398,0.0002763988,0.0003433833,0.0001996884],"domain_scores_gemma":[0.9946641,0.002294078,0.001118524,0.0004426362,0.0006738026,0.0008068915],"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":[0.0001125066,0.00008471109,0.002905668,0.0001229184,0.00007908716,0.0009731153,0.0004524509,0.0272705,0.006728059,0.9545313,0.001050104,0.005689619],"study_design_scores_gemma":[0.00007790025,0.0001434514,0.004331529,0.00008678195,0.00005230903,0.0009465441,0.0002124226,0.5209279,0.003338768,0.4675443,0.002232244,0.0001058816],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8126668,0.00132543,0.1720819,0.0007273275,0.0001630037,0.00009725067,0.0001495272,0.000251986,0.01253661],"genre_scores_gemma":[0.984076,0.0003326315,0.01210342,0.00009930688,0.000119391,0.0001298425,0.00009499572,0.00004631029,0.002997987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002312773,"threshold_uncertainty_score":0.0106945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07387331019405094,"score_gpt":0.3082274222451087,"score_spread":0.2343541120510578,"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."}}