{"id":"W4310960902","doi":"10.1002/eap.2783","title":"Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis","year":2022,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Reserve System, University of California; University of California, Davis; Bureau of Reclamation; California Department of Transportation; National Science Foundation","keywords":"Population viability analysis; Threatened species; Population; Ecology; Extinction (optical mineralogy); Biodiversity; Biology; Climate change; Habitat; Bayesian probability; Environmental resource management; Environmental science; Statistics; Endangered species; Mathematics; Demography","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":[],"consensus_categories":[],"category_scores_codex":[0.003260193,0.0005975501,0.0006892318,0.000812381,0.0004938839,0.001270019,0.001611809,0.0008696471,0.001451682],"category_scores_gemma":[0.009626803,0.0006375572,0.001007601,0.0007319109,0.001026113,0.001067574,0.00177391,0.001291415,0.0001415953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028836,"about_ca_system_score_gemma":0.001485788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01585558,"about_ca_topic_score_gemma":0.0139053,"domain_scores_codex":[0.9991238,0.0005212759,0.00003878749,0.0001362275,0.0001132233,0.00006666924],"domain_scores_gemma":[0.9973182,0.002004415,0.0002859903,0.00009250181,0.0002063669,0.00009241627],"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":[0.00001019934,0.00001033086,0.002408572,0.00001517305,0.00003960673,0.0000359249,0.00004467976,0.9636529,0.0001631711,0.02599484,0.0001790714,0.00744564],"study_design_scores_gemma":[0.000002088717,0.000004024218,0.0002167511,0.000002876731,0.000004364101,0.000007474277,0.000007018127,0.9865226,0.00003212877,0.01302996,0.000166341,0.000004359786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04529717,0.0001456042,0.952278,0.0002933714,0.00001750965,0.00004084415,0.0001574357,0.0001532206,0.001616902],"genre_scores_gemma":[0.7873483,0.000391195,0.2090653,0.0001554891,0.00007532421,0.0002861268,0.000365407,0.00009688242,0.00221588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01585558,"threshold_uncertainty_score":0.03152657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555171503880566,"score_gpt":0.2678851250517649,"score_spread":0.2423334100129592,"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."}}