{"id":"W2918963660","doi":"10.1101/568725","title":"Evaluating genomic data for management of local adaptation in a changing climate: A lodgepole pine case study","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates Bio Solutions; Ministry of Forests, Lands and Natural Resource Operations; Genome British Columbia; Alberta Innovates; Forest Genetics Council of British Columbia; Genome Canada","keywords":"Pinus contorta; Local adaptation; Adaptation (eye); Biology; Spatial ecology; Climate change; Spatial variability; Ecology; Geography; Statistics; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002044134,0.0003402866,0.0004354881,0.0002127108,0.0001300391,0.00007270051,0.0006853738,0.000160487,0.0007381765],"category_scores_gemma":[0.00003596105,0.0003838009,0.00006909371,0.0006122561,0.00008647501,0.0001703096,0.00315947,0.0002297761,0.0001050689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136137,"about_ca_system_score_gemma":0.00005788594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004395521,"about_ca_topic_score_gemma":0.0001462347,"domain_scores_codex":[0.9971719,0.0001098527,0.0006325638,0.001050524,0.000417207,0.0006180019],"domain_scores_gemma":[0.9977593,0.0000452305,0.0004537689,0.001589904,0.00005629871,0.00009554081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001946413,0.01580405,0.306071,0.02157213,0.002449605,0.007060938,0.003645123,0.1966508,0.4369463,0.004532278,0.001830636,0.001490785],"study_design_scores_gemma":[0.008675078,0.0008198591,0.4665252,0.001313688,0.0009461172,8.696995e-7,0.0215901,0.4888754,0.007211438,0.000002022284,0.001498066,0.002542238],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872763,0.0001812496,0.008107808,0.00003433134,0.0003905013,0.002742056,0.001175154,0.00006803308,0.00002461834],"genre_scores_gemma":[0.9967436,0.00008262749,0.002623234,0.00003760323,0.00004197179,0.0003984666,0.000009632181,0.00005912613,0.000003768074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4297349,"threshold_uncertainty_score":0.9998614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08643490862179704,"score_gpt":0.3107222359083486,"score_spread":0.2242873272865516,"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."}}