{"paper":{"title":"When do trajectories matter? Identifiability analysis for stochastic transport phenomena","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Collecting individual trajectories resolves structural non-identifiability in stochastic diffusion models when count data alone fails.","cross_cats":["q-bio.QM","stat.AP"],"primary_cat":"nlin.CG","authors_text":"Matthew J Simpson, Michael J Plank","submitted_at":"2026-04-17T00:37:47Z","abstract_excerpt":"Stochastic models of diffusion are routinely used to study dispersal of populations, including populations of animals, plants, seeds and cells. Advances in imaging and field measurement technologies mean that data are often collected across a range of scales, including count data collected across a series of fixed sampling regions to characterize population-level dispersal, as well as individual trajectory data to examine at the motion of individuals within a diffusive population. In this work we consider a lattice-based random walk model and examine the extent to which model parameters can be"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"working with count data alone can sometimes lead to challenges involving structural non-identifiability that can be alleviated by collecting trajectory data","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The analysis assumes that the lattice-based random walk model accurately represents the underlying stochastic transport phenomena and that the mean-field PDE approximations are valid for the parameter regimes considered.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Trajectory data resolves structural non-identifiability in lattice random walk diffusion models that count data alone cannot, with analysis of experimental design impacts on practical identifiability.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Collecting individual trajectories resolves structural non-identifiability in stochastic diffusion models when count data alone fails.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"bb9045567cc0dc35d2305f6d703f8325698c0e480606c8faf1ee3cc67eafaa88"},"source":{"id":"2604.15598","kind":"arxiv","version":2},"verdict":{"id":"3935a4ba-07c3-40c2-b063-da386e94af87","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T17:10:03.635746Z","strongest_claim":"working with count data alone can sometimes lead to challenges involving structural non-identifiability that can be alleviated by collecting trajectory data","one_line_summary":"Trajectory data resolves structural non-identifiability in lattice random walk diffusion models that count data alone cannot, with analysis of experimental design impacts on practical identifiability.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The analysis assumes that the lattice-based random walk model accurately represents the underlying stochastic transport phenomena and that the mean-field PDE approximations are valid for the parameter regimes considered.","pith_extraction_headline":"Collecting individual trajectories resolves structural non-identifiability in stochastic diffusion models when count data alone fails."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.15598/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":78,"sample":[{"doi":"10.1017/cbo9780511755767","year":1999,"title":"Introduction to Econophysics: Correlations and Complexity in Finance","work_id":"3b70f098-8569-498d-8e94-ba2b16e55a17","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1017/cbo9780511606014","year":2001,"title":"Cambri dge University Press, Cambridge (2001)","work_id":"da3a3de3-0ed4-49f8-a6db-eee9891f65af","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1007/b98868","year":2002,"title":"An Introduction , 3rd edn","work_id":"e2ce102d-e077-459d-82af-a5e91b007563","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1983,"title":"Random Walks in Biology","work_id":"a5664df4-b846-4bd6-b099-1a274f13c80c","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1098/rsif.2008.0014","year":2008,"title":"Random walk models in biology","work_id":"b5e7c149-b74c-422c-97e5-5c4f4bbac0c1","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":78,"snapshot_sha256":"26b7e63e92b17af6a6ff13aaac8f650a46f3b4618f8aa8f05dfed0ef6a778f51","internal_anchors":1},"formal_canon":{"evidence_count":2,"snapshot_sha256":"094c019740de4475d6dbb5937e6072b5aeaeb1e139cbd50003d9b82be73fa45d"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}