{"id":"W2892015805","doi":"10.1002/jwmg.21567","title":"Estimates of tidal‐marsh bird densities using Bayesian networks","year":2018,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Forest Service; U.S. Geological Survey; National Science Foundation of Sri Lanka; U.S. Fish and Wildlife Service","keywords":"Sparrow; Salt marsh; Marsh; Geography; Habitat; Ecology; Population; Abundance (ecology); Distance sampling; Wetland; Biology; Demography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008169084,0.0001524936,0.0003041479,0.0001205504,0.00009012987,0.00004059021,0.0003281618,0.00004745124,0.0002980753],"category_scores_gemma":[0.00002949104,0.0001285669,0.0001093268,0.0002485426,0.0001974417,0.000271918,0.0002364655,0.0001074719,0.00006029876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001996666,"about_ca_system_score_gemma":0.000006878888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001940098,"about_ca_topic_score_gemma":0.00005903637,"domain_scores_codex":[0.9984311,0.00007416245,0.000566046,0.0001468299,0.0005124183,0.0002694629],"domain_scores_gemma":[0.9989731,0.00006349826,0.0005883627,0.0002364302,0.00003286127,0.0001057234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004265845,0.0006774958,0.5987665,0.0005624973,0.001170915,0.0006963623,0.001694734,0.1748941,0.00432759,0.0004942782,0.1456036,0.07068526],"study_design_scores_gemma":[0.001552077,0.001256346,0.1536944,0.001125114,0.0004505895,0.0002642323,0.000717201,0.8182831,0.001274044,0.0006664121,0.02010052,0.0006159933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9273863,0.00009412853,0.06718522,0.0003623388,0.001108678,0.0002702589,9.412215e-7,0.00003168069,0.003560482],"genre_scores_gemma":[0.981362,0.0000265721,0.01770114,0.00046418,0.0003080168,0.000001119891,1.905204e-7,0.00002950506,0.0001072681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6433889,"threshold_uncertainty_score":0.5242807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008678812704657335,"score_gpt":0.2281004956191524,"score_spread":0.2194216829144951,"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."}}