{"id":"8be53bb1-3c56-4984-a83c-0d4e01a825b2","arxiv_id":"2509.24977","paper_version":3,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A bridge-based Monte Carlo method for path likelihoods reveals sharp sampling-resolution limits beyond which competing stochastic population models cannot be told apart.","lead":"This paper develops a Monte Carlo method that combines bridge processes and full-path statistics to fit stochastic population models from sparsely sampled time series. It maps out when data resolution is sufficient to tell competing models apart, and applies the method to microbiome, forest, social-media and optical-tweezer data.","discovery_kind":"new_method","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-04T13:52:30.424822+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}