{"id":"W4416541551","doi":"10.48550/arxiv.2504.11835","title":"Particle Data Cloning for Complex Ordinary Differential Equations","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simon Fraser University","keywords":"Ode; Ordinary differential equation; Frequentist inference; Particle filter; Inference; Cloning (programming); Statistical inference; Global optimization","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001849007,0.0002605047,0.0003249471,0.00007418958,0.0003036257,0.0003585941,0.003685135,0.0001523771,0.00005486234],"category_scores_gemma":[0.0001971899,0.0002507603,0.00009874952,0.0002525628,0.00005883883,0.0003934856,0.006186524,0.0003389403,0.00004344565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003800427,"about_ca_system_score_gemma":0.0003896016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007376508,"about_ca_topic_score_gemma":0.00003587676,"domain_scores_codex":[0.9979089,0.00005224242,0.0004070434,0.00102237,0.0001983016,0.0004111458],"domain_scores_gemma":[0.9970058,0.0002562586,0.0002078929,0.002277394,0.0001489686,0.0001036842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000116146,0.00164589,0.288482,0.004028351,0.0009281411,0.00007040479,0.002610525,0.001733011,0.00489181,0.5209404,0.02990559,0.1446477],"study_design_scores_gemma":[0.000510094,0.00007361716,0.08816788,0.0003089635,0.00009534745,0.00000296848,0.00002217553,0.8857242,0.0007440439,0.01862854,0.005154854,0.0005673253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03184234,0.0001882432,0.9631369,0.002623373,0.0008889583,0.0004038956,0.0002220672,0.0002231252,0.0004711732],"genre_scores_gemma":[0.9542699,0.00002162222,0.04391015,0.0002840578,0.0002202657,0.0001126415,0.0004062654,0.00001142805,0.0007636577],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9224276,"threshold_uncertainty_score":0.9999945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2105553698004423,"score_gpt":0.3585128331703055,"score_spread":0.1479574633698632,"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."}}