{"id":"W4393386859","doi":"10.1111/gcb.17227","title":"How useful is genomic data for predicting maladaptation to future climate?","year":2024,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Natural Resources Canada; University of Calgary; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Alberta Innovates Bio Solutions; University of British Columbia; Genome British Columbia; University of Alberta; Génome Québec; Compute Canada","keywords":"Maladaptation; Climate change; Offset (computer science); Population; Ecology; Geography; Computer science; Biology; Demography","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":[0.007556189,0.0007559068,0.0005984314,0.001508189,0.0003490604,0.002443881,0.0007132606,0.0008803756,0.001277923],"category_scores_gemma":[0.01908424,0.0002227211,0.0006329981,0.001693157,0.0007327561,0.001593973,0.0007006851,0.001089016,0.0004103915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004614248,"about_ca_system_score_gemma":0.0004605374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004330075,"about_ca_topic_score_gemma":0.007239906,"domain_scores_codex":[0.997932,0.001355783,0.00007803019,0.0003988456,0.0001588722,0.00007660126],"domain_scores_gemma":[0.9821005,0.01308263,0.001430994,0.001866242,0.001212998,0.0003064792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002697223,0.00008969894,0.8311675,0.0001426886,0.0009683195,0.00007750776,0.0001578264,0.110433,0.005271485,0.00150501,0.001439151,0.04847801],"study_design_scores_gemma":[0.00004223561,0.0002005499,0.4608568,0.0002438313,0.0003909806,0.0001772303,0.0006644686,0.5028641,0.006409945,0.02281277,0.005217587,0.000119508],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9101029,0.002091542,0.07514068,0.002525922,0.00013183,0.00003782053,0.006229981,0.0005240507,0.003215343],"genre_scores_gemma":[0.9837307,0.0003117354,0.01335505,0.000272542,0.00004621217,0.00001579109,0.002052111,0.00004692549,0.0001689642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007556189,"threshold_uncertainty_score":0.0399614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1041025093451634,"score_gpt":0.3178763799015952,"score_spread":0.2137738705564319,"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."}}