{"id":"W2944134989","doi":"10.1111/gcb.14681","title":"Modeling optimal responses and fitness consequences in a changing Arctic","year":2019,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Alberta Innovates; Killam Trusts; Canada Research Chairs; ArcticNet; Quark Expeditions; World Wildlife Fund; National Science Foundation","keywords":"Arctic; Climate change; Environmental science; Ecology; Biology","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":[0.0007049288,0.0006439565,0.0005573547,0.0004241924,0.0005272517,0.0009455871,0.0007821904,0.001246032,0.001355862],"category_scores_gemma":[0.001891009,0.0005194556,0.0009962019,0.0003121899,0.0006692165,0.0005182457,0.0005866977,0.000769283,0.0001043754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002106917,"about_ca_system_score_gemma":0.001903979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09010656,"about_ca_topic_score_gemma":0.06133874,"domain_scores_codex":[0.9997764,0.00007968813,0.000006290251,0.00004904281,0.00001683259,0.00007169371],"domain_scores_gemma":[0.9993874,0.0003608138,0.0001013356,0.00001844254,0.00006166507,0.00007018972],"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":[0.00001009882,0.000007948594,0.001247806,0.000004964118,0.00001112928,0.00001706137,0.000009618709,0.9974334,0.0002024356,0.0007534832,0.00003554206,0.0002666692],"study_design_scores_gemma":[0.000004824977,0.00001141965,0.0007583438,0.000002847567,0.00001008335,0.000005306265,0.00001591955,0.9981034,0.00006087159,0.0009182321,0.0001046331,0.000004057102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9383136,0.0002042026,0.05413982,0.0005960266,0.00003888728,0.0000296405,0.0004836331,0.00008083562,0.006113268],"genre_scores_gemma":[0.9888175,0.0001467902,0.008450167,0.0000777916,0.00001765839,0.0000541446,0.0001974181,0.00002613586,0.002212449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09010656,"threshold_uncertainty_score":0.1791641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03723939761620132,"score_gpt":0.2870287298327036,"score_spread":0.2497893322165023,"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."}}