{"id":"W4389085847","doi":"10.1016/j.tree.2023.10.012","title":"Recommendations for quantitative uncertainty consideration in ecology and evolution","year":2023,"lang":"en","type":"review","venue":"Trends in Ecology & Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Jane ja Aatos Erkon Säätiö; European Research Council; Norges Forskningsråd; Horizon 2020; HORIZON EUROPE European Research Council; Academy of Finland","keywords":"Propagation of uncertainty; Sensitivity analysis; Uncertainty analysis; Uncertainty quantification; Computer science; Ecology; Field (mathematics); Key (lock); Uncertainty; Focus (optics); Management science; Data science; Econometrics; Risk analysis (engineering); Machine learning; Mathematics; Engineering; Statistics; Biology; Business; Simulation; Algorithm; Physics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0341353,0.00257891,0.004899815,0.01174289,0.001354858,0.006878095,0.00678817,0.008468894,0.0321929],"category_scores_gemma":[0.09760435,0.00163736,0.006216875,0.01314661,0.002834277,0.01343033,0.004530862,0.01021691,0.01696762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005992474,"about_ca_system_score_gemma":0.0256309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01097176,"about_ca_topic_score_gemma":0.01335286,"domain_scores_codex":[0.9840333,0.005895056,0.003616805,0.001151631,0.004730023,0.0005732325],"domain_scores_gemma":[0.8982502,0.04631189,0.008952697,0.003577625,0.04022909,0.002678552],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006673466,0.00008369172,0.0001480333,0.05330457,0.0002498857,0.0001182576,0.0003002982,0.000574895,0.0002797272,0.01746318,0.5296313,0.3977794],"study_design_scores_gemma":[0.00003977557,0.00002036398,0.0003266238,0.05287099,0.0002614549,0.00009879696,0.0001436098,0.0001084758,0.00006982382,0.01576241,0.9302509,0.00004686251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001642344,0.8247925,0.008220334,0.1213514,0.02654287,0.0009786253,0.002921358,0.0005265322,0.0145023],"genre_scores_gemma":[0.001840584,0.8827292,0.03943759,0.05106409,0.005381417,0.002412804,0.003708966,0.0002233182,0.01320207],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9658647,"threshold_uncertainty_score":0.1805269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1602468535945329,"score_gpt":0.3998432271347572,"score_spread":0.2395963735402243,"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."}}