{"id":"W4393507296","doi":"10.5281/zenodo.901702","title":"Supplementary Material 1 From: Yemshanov D, Koch F, Ducey M, Haack R, Siltanen M, Wilson K (2013) Quantifying Uncertainty In Pest Risk Maps And Assessments: Adopting A Risk-Averse Decision Maker'S Perspective. Neobiota 18: 193-218. Https://Doi.Org/10.3897/Neobiota.18.4002","year":2013,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Decision maker; PEST analysis; Mathematics; Agricultural science; Operations research; Biology; Botany","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":[0.0008597018,0.001405444,0.0009289887,0.002342457,0.0005148803,0.002022425,0.002002768,0.001590207,0.1793617],"category_scores_gemma":[0.005912848,0.0006140057,0.0008237911,0.004588109,0.0002697511,0.001518447,0.001471243,0.001294154,0.1365291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001578523,"about_ca_system_score_gemma":0.001469614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01841118,"about_ca_topic_score_gemma":0.03340916,"domain_scores_codex":[0.9994619,0.00007687939,0.00008020827,0.0001471785,0.0001407019,0.00009309337],"domain_scores_gemma":[0.9981792,0.0006618234,0.0001903179,0.0003466078,0.000488997,0.0001331773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003290471,0.00001836807,0.001114252,0.0006568967,0.00002167774,0.00001723169,0.00001614857,0.0003637953,0.00008909617,0.000473418,0.9948644,0.002331974],"study_design_scores_gemma":[0.0002517188,0.00001640341,0.006493053,0.0003794559,0.0000239242,0.00006369975,0.00007529625,0.0005993098,0.0003116029,0.002237622,0.9895223,0.0000256821],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005546624,0.00002102418,0.00004913549,0.0000301483,0.000009253872,0.000005279264,0.9992746,0.000122017,0.0004332247],"genre_scores_gemma":[0.0004349261,0.00004049017,0.0003055885,0.00003467274,0.00000453906,0.0000583936,0.9985107,0.00005614656,0.0005545696],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1793617,"threshold_uncertainty_score":0.6000247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06254715178935427,"score_gpt":0.2972982777185224,"score_spread":0.2347511259291681,"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."}}