{"id":"W4320925302","doi":"10.5751/ace-02357-180104","title":"Using Breeding Bird Survey and eBird data to improve marsh bird monitoring abundance indices and trends","year":2023,"lang":"en","type":"article","venue":"Avian Conservation and Ecology","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Birds Canada","funders":"Environment and Climate Change Canada; Nature Conservancy of Canada; Government of Ontario; TD Friends of the Environment Foundation","keywords":"Marsh; Breeding bird survey; Abundance (ecology); Habitat; Ecology; Geography; Population; Citizen science; Bird conservation; Survey methodology; Biology; Wetland; Demography; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004225055,0.0005103214,0.0003961307,0.004178816,0.0004400403,0.001034548,0.0007085794,0.0002311756,0.001140481],"category_scores_gemma":[0.01090175,0.0003764674,0.0003411833,0.004852131,0.0001988848,0.001094575,0.0007516594,0.0003605691,0.0004351344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001790928,"about_ca_system_score_gemma":0.002299197,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.530844,"about_ca_topic_score_gemma":0.8014104,"domain_scores_codex":[0.9981769,0.0003753214,0.0002667527,0.000403181,0.0006021783,0.0001755417],"domain_scores_gemma":[0.9917592,0.001200015,0.002458367,0.0006874951,0.003563772,0.0003311667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002873023,0.00001354717,0.9710975,0.0000706002,0.00008463803,0.00002125083,0.0002933819,0.0008253456,0.001017658,0.00006163094,0.001056344,0.02542948],"study_design_scores_gemma":[0.000004871017,0.00002202961,0.9942616,0.00002842728,0.00004059291,0.00002426601,0.0001939313,0.002888083,0.0003304034,0.00003924982,0.002158677,0.000007841688],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367936,0.0009285689,0.02595924,0.0002817098,0.00005580187,0.0003045817,0.02507888,0.0005427774,0.01005485],"genre_scores_gemma":[0.929236,0.0005997624,0.04951686,0.000133012,0.00004734061,0.0002645516,0.0183304,0.00008091979,0.001791213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.530844,"threshold_uncertainty_score":0.9438378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120073783581,"score_gpt":0.3138542077238171,"score_spread":0.2018468293657171,"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."}}