{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001568276,0.0001263938,0.0001108409,0.00001671573,0.00009982702,0.0001124302,0.000362545,0.000121736,0.00356646],"category_scores_gemma":[0.00002149639,0.0001108534,0.00004045995,0.0002145915,0.00004991431,0.0002506835,0.0005350322,0.00004657431,0.0008391246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004613999,"about_ca_system_score_gemma":0.000006827486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001326905,"about_ca_topic_score_gemma":0.0005473068,"domain_scores_codex":[0.9989004,0.00002061813,0.0001065737,0.000510748,0.00007769142,0.0003839956],"domain_scores_gemma":[0.9995285,0.00001709917,0.00002956227,0.0003201995,0.000008348136,0.00009624219],"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":[0.0001787628,0.00009537467,0.4236956,0.0002026918,0.00008785983,0.00001652511,0.001776182,9.355932e-7,0.006486985,0.02692288,0.4280145,0.1125217],"study_design_scores_gemma":[0.0001158462,0.000102785,0.1129054,0.000007487683,0.00001811178,0.00001102781,0.001721922,0.0007299339,0.00001723975,0.0001536835,0.8840809,0.0001356472],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8926429,0.002494679,0.004302053,0.04166859,0.004715335,0.00150821,0.04685086,0.0004203915,0.005396986],"genre_scores_gemma":[0.991718,0.0002945297,0.0004552076,0.002784287,0.001184438,0.0001495408,0.003279413,0.00001371943,0.0001208612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4560663,"threshold_uncertainty_score":0.9999388,"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."}}