{"id":"W4403575206","doi":"10.48550/arxiv.2410.11777","title":"Measure estimation on a manifold explored by a diffusion process","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Labex Bézout; Agence Nationale de la Recherche; European Commission","keywords":"Measure (data warehouse); Estimation; Diffusion; Diffusion process; Manifold (fluid mechanics); Process (computing); Mathematics; Computer science; Statistical physics; Data mining; Economics; Physics; Innovation diffusion; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005086463,0.0004646469,0.0004861424,0.0002687624,0.0001136711,0.00008790053,0.0004960319,0.0004829682,0.00003528823],"category_scores_gemma":[0.0002612748,0.0004629013,0.0002923443,0.0003416962,0.00004280407,0.00007580953,0.0006484764,0.0009014041,0.000005211899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002812915,"about_ca_system_score_gemma":0.0001159055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003372376,"about_ca_topic_score_gemma":0.0000266697,"domain_scores_codex":[0.9979762,0.0002102343,0.0002685806,0.001032372,0.0001981353,0.0003144921],"domain_scores_gemma":[0.9983655,0.0002242033,0.0002490515,0.0008341789,0.0001602063,0.0001668669],"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":[0.001174319,0.001906032,0.0002737221,0.01179848,0.001065284,0.00170392,0.006606867,0.02552348,0.00179383,0.8829681,0.053278,0.01190798],"study_design_scores_gemma":[0.0009568409,0.0001640958,0.000008386755,0.002150389,0.0006810054,0.000007183042,0.0008882562,0.4282008,0.001363572,0.5638785,0.0006858124,0.001015183],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8543346,0.0001537972,0.1262347,0.0001646098,0.0007597965,0.0009861826,0.0001108914,0.0005910711,0.01666436],"genre_scores_gemma":[0.9906878,0.00008639935,0.001743655,0.00005858017,0.00009496533,0.000009834775,0.00005640537,0.00008392242,0.007178406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4026773,"threshold_uncertainty_score":0.9997823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1629331584894465,"score_gpt":0.2625514235409761,"score_spread":0.09961826505152963,"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."}}