{"id":"W4405468205","doi":"10.48550/arxiv.2412.10592","title":"Self-Exciting Random Evolutions (SEREs) and their Applications (Version 2)","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","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":[0.001173682,0.0006546483,0.0006939129,0.0008486001,0.0004317441,0.001020022,0.0007379681,0.001237235,0.004696027],"category_scores_gemma":[0.003707059,0.0003092896,0.001696247,0.0006806186,0.00131514,0.001782772,0.00122007,0.002057626,0.0006456779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005751144,"about_ca_system_score_gemma":0.0004644474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163101,"about_ca_topic_score_gemma":0.000498093,"domain_scores_codex":[0.999413,0.0001881685,0.00004093028,0.0001449011,0.0001541532,0.00005872802],"domain_scores_gemma":[0.9986811,0.0006324834,0.0002100371,0.0001500362,0.0002326285,0.00009367738],"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":[0.00001590796,0.00002224331,0.0006809416,0.0001501978,0.00004424547,0.0001884551,0.0001213142,0.02814265,0.001740336,0.9534191,0.001721807,0.01375274],"study_design_scores_gemma":[0.00001880903,0.0000791107,0.00117998,0.00007460886,0.00003206437,0.0005927373,0.00004838524,0.305957,0.000975877,0.6644158,0.02658331,0.00004223638],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04790095,0.01251109,0.8950884,0.002053724,0.001075224,0.00007464526,0.0002824542,0.0002930423,0.04072047],"genre_scores_gemma":[0.7636845,0.01448764,0.1514647,0.001528176,0.002620373,0.0003707126,0.0005284302,0.0003457677,0.06496975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004696027,"threshold_uncertainty_score":0.01570982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02734859515317418,"score_gpt":0.1776137444962644,"score_spread":0.1502651493430902,"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."}}