{"paper":{"title":"A Double Machine Learning Trend Model for Citizen Science Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Alison Johnston (2), Amanda D. Rodewald (1) ((1) Cornell Lab of Ornithology, Chris Wood (1), Cornell University, Daniel Fink (1), Environmental Modelling, Lauren Oldham Jaromczyk (1), Matt Strimas-Mackey (1), Orin Robinson (1), School of Maths, Shawn Ligocki (1), St Andrews, Statistics, Steve Kelling (1), Tom Auer (1), UK), University of St Andrews, USA (2) Centre for Research into Ecological, Wesley M. Hochachka (1)","submitted_at":"2022-10-27T15:08:05Z","abstract_excerpt":"1. Citizen and community-science (CS) datasets have great potential for estimating interannual patterns of population change given the large volumes of data collected globally every year. Yet, the flexible protocols that enable many CS projects to collect large volumes of data typically lack the structure necessary to keep consistent sampling across years. This leads to interannual confounding, as changes to the observation process over time are confounded with changes in species population sizes.\n  2. Here we describe a novel modeling approach designed to estimate species population trends wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15524","kind":"arxiv","version":2},"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/2210.15524/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"}