{"paper":{"title":"Spatial Model Selection and Uncertainty Quantification: Comparing Continuous and Discrete Wound Healing Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Jana L. Gevertz, John T. Nardini","submitted_at":"2026-06-09T13:50:23Z","abstract_excerpt":"All data-driven modeling tasks (e.g., parameter estimation, uncertainty quantification, and data forecasting) require the selection of a mathematical model. An overlooked aspect of model selection is modality; for example, there are no guidelines on when to use a partial differential equation (PDE) model or an agent-based model (ABM) for spatial processes. To address this, we created a model selection pipeline that uses approximate Bayesian computations to perform parameter estimation, uncertainty quantification, and model selection (using both information criteria and out-of-sample forecastin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10873","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.10873/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}