{"id":"W4412849866","doi":"10.1002/ece3.73755","title":"Too Few, Too Many, or Just Right? Optimizing Sample Sizes for Population‐Level Inferences in Animal Tracking Projects","year":2025,"lang":"en","type":"preprint","venue":"Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"Israel Institute for Biological Research; Sächsisches Staatsministerium für Wissenschaft und Kunst; Bundesministerium für Bildung und Forschung; National Science Foundation","keywords":"Sample (material); Tracking (education); Population; Computer science; Statistics; Mathematics; Psychology; Demography; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05078872,0.0009373659,0.001199344,0.001589197,0.001311401,0.002466605,0.00295367,0.001708252,0.004651157],"category_scores_gemma":[0.1504219,0.0009869947,0.001251719,0.001451586,0.001795906,0.003093432,0.002829748,0.002442539,0.001081946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001805889,"about_ca_system_score_gemma":0.00568571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009359568,"about_ca_topic_score_gemma":0.01427922,"domain_scores_codex":[0.9859686,0.009944168,0.0007934225,0.00142398,0.00140029,0.0004695639],"domain_scores_gemma":[0.8938285,0.0871047,0.004655967,0.006833595,0.005756387,0.001820898],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001258295,0.0006758182,0.06348738,0.001128885,0.0004373423,0.0003703559,0.002176634,0.4524314,0.01080944,0.07189152,0.01094576,0.3843871],"study_design_scores_gemma":[0.0007179034,0.0005584323,0.01150865,0.0006387845,0.0002171118,0.0001980016,0.0005869853,0.8123358,0.01242371,0.1477518,0.01290445,0.0001584236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0370483,0.0003150922,0.956547,0.0009021005,0.00007940184,0.0009003631,0.0004987653,0.001450313,0.002258694],"genre_scores_gemma":[0.2007297,0.0001769388,0.7960091,0.0003048261,0.00002735889,0.001544588,0.0003720889,0.000194149,0.0006412648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9492113,"threshold_uncertainty_score":0.2685996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05587829394225013,"score_gpt":0.29197349958293,"score_spread":0.2360952056406799,"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."}}