{"id":"W4399106128","doi":"10.1186/s12862-024-02260-z","title":"Identifying potential provenances for climate-change adaptation using spatially variable coefficient models","year":2024,"lang":"en","type":"article","venue":"BMC Ecology and Evolution","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro Geotech (Canada); Alberta Biodiversity Monitoring Institute","funders":"Alexander von Humboldt-Stiftung","keywords":"Ecotype; Climate change; Cluster analysis; Variable (mathematics); Adaptation (eye); Ecological niche; Ecology; Computer science; Statistics; Biology; Mathematics","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":[0.004049402,0.0006413216,0.000557626,0.001665217,0.0007631897,0.001372615,0.001469356,0.0008640079,0.002314579],"category_scores_gemma":[0.01210187,0.0003644165,0.00252235,0.001277769,0.001152459,0.00108106,0.001075662,0.00117828,0.0003454209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173383,"about_ca_system_score_gemma":0.0008355635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01417966,"about_ca_topic_score_gemma":0.0215248,"domain_scores_codex":[0.9985254,0.0007200374,0.00006926325,0.0005074173,0.0000828967,0.00009502604],"domain_scores_gemma":[0.9898565,0.007483605,0.001123449,0.0008164446,0.0005069759,0.0002130386],"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":[0.0002374734,0.000124573,0.2433247,0.000163735,0.0008742188,0.0002836909,0.0004991991,0.7071351,0.005531438,0.01475936,0.001325389,0.02574112],"study_design_scores_gemma":[0.00001680603,0.00003289788,0.03285673,0.00002447586,0.000103374,0.00008872464,0.00009661041,0.953693,0.0004732772,0.01191107,0.0006724891,0.00003049415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7956648,0.0002402381,0.2006438,0.0003357528,0.00002618399,0.00006427323,0.0008993027,0.0003815937,0.00174401],"genre_scores_gemma":[0.9753038,0.00007087545,0.0232654,0.00004138668,0.00001181802,0.00003919175,0.0007653176,0.00008297266,0.0004192599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01417966,"threshold_uncertainty_score":0.02819431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0491080118780783,"score_gpt":0.2682011711564998,"score_spread":0.2190931592784215,"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."}}