{"id":"W3092468001","doi":"10.1111/2041-210x.13509","title":"Exploiting the full potential of Bayesian networks in predictive ecology","year":2020,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Ministry of Environment; Alberta Machine Intelligence Institute; Ministry of Environment - Saskatchewan; Alberta Environment and Parks; Alberta Agriculture and Forestry; Canadian Forest Service; Ontario Ministry of Natural Resources and Forestry; Natural Resources Canada; Ministry of Natural Resources","keywords":"Covariate; Missing data; Bayesian network; Computer science; Bayesian probability; Machine learning; Probabilistic logic; Artificial intelligence; Data mining; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00918485,0.001517881,0.001236595,0.003187793,0.0009878192,0.002319226,0.001886958,0.002117339,0.002777457],"category_scores_gemma":[0.04570297,0.001404119,0.001466757,0.001786624,0.002258368,0.006120289,0.002538291,0.004061185,0.0005962893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002085305,"about_ca_system_score_gemma":0.001509002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01390913,"about_ca_topic_score_gemma":0.01603099,"domain_scores_codex":[0.9959513,0.002720884,0.000141064,0.0005352774,0.000492772,0.0001587611],"domain_scores_gemma":[0.9409262,0.0538208,0.001689705,0.001668368,0.001471599,0.0004233592],"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":[0.00008371933,0.00006962035,0.005475172,0.0001295719,0.00009604704,0.000112019,0.0002583337,0.8988681,0.0005429627,0.03745991,0.00118761,0.05571702],"study_design_scores_gemma":[0.000005659678,0.000008614651,0.0002626468,0.00002888607,0.000008310014,0.00001310427,0.00001270101,0.9487227,0.0001345645,0.05041644,0.0003780687,0.000008332886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03030512,0.0007649439,0.9640182,0.001433938,0.00003629175,0.00005267478,0.0002347512,0.0005561573,0.002597854],"genre_scores_gemma":[0.7594893,0.001111309,0.2357453,0.0005463672,0.0001885039,0.0001943753,0.0007490526,0.0001844324,0.001791369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01390913,"threshold_uncertainty_score":0.04857475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390493668453782,"score_gpt":0.2935166226047527,"score_spread":0.2696116859202149,"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."}}