{"id":"W2982474944","doi":"10.5751/es-11169-240411","title":"Recovery planning in a dynamic system: integrating uncertainty into a decision support tool for an endangered songbird","year":2019,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Fish and Wildlife Service","keywords":"Endangered species; Songbird; Decision support system; Nature Conservation; Ecology; Computer science; Environmental resource management; Geography; Biology; Habitat; Artificial intelligence; Environmental science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004544442,0.001014466,0.0006602565,0.001520026,0.001410719,0.003554074,0.001685122,0.001646551,0.005378032],"category_scores_gemma":[0.01078973,0.0005295991,0.0006325754,0.0006917373,0.0008410396,0.004265161,0.00344259,0.001576903,0.0006804765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001516354,"about_ca_system_score_gemma":0.002543756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008030589,"about_ca_topic_score_gemma":0.01235788,"domain_scores_codex":[0.998828,0.0005027077,0.0001043955,0.0001760159,0.0002653306,0.0001236071],"domain_scores_gemma":[0.9940664,0.004049776,0.0005051511,0.0001946545,0.0005845113,0.0005994343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000886847,0.0005996228,0.01195272,0.0004650002,0.0002189956,0.001685699,0.002245451,0.5898131,0.006854928,0.02064874,0.01819152,0.3464375],"study_design_scores_gemma":[0.0001165694,0.0003509497,0.002547001,0.0002744033,0.0001869632,0.0003352431,0.001597747,0.9435412,0.002765078,0.0305281,0.01760315,0.0001535763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2313363,0.001372972,0.7116975,0.01246802,0.0003441676,0.0007059437,0.001220076,0.009322657,0.0315323],"genre_scores_gemma":[0.7105956,0.0005407701,0.2851852,0.0004824332,0.000099176,0.0002449833,0.0004543071,0.0002134609,0.002184069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008030589,"threshold_uncertainty_score":0.02403355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00704729165601416,"score_gpt":0.2472439879618761,"score_spread":0.2401966963058619,"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."}}