{"id":"W6894438672","doi":"10.5683/sp3/szaljr","title":"On the fast track: Hybrids adapt more rapidly than parental populations in a novel environment","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Academy of Finland","keywords":"Hybrid; Adaptation (eye); Genetic Fitness; Population; Offset (computer science); Climate change","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":[0.001145193,0.001461712,0.001006955,0.00129343,0.001005939,0.001638817,0.002322994,0.002006551,0.02807537],"category_scores_gemma":[0.005113321,0.0005112913,0.001449855,0.002076315,0.0004062794,0.001092048,0.001059024,0.001206179,0.01756581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032081,"about_ca_system_score_gemma":0.0008492453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03069505,"about_ca_topic_score_gemma":0.06888404,"domain_scores_codex":[0.9994729,0.0001354986,0.00004620728,0.0001816157,0.00009252626,0.00007123726],"domain_scores_gemma":[0.9984031,0.0007681461,0.0001309147,0.000328812,0.0002597407,0.0001092866],"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":[0.0002542775,0.00008284024,0.01960559,0.001283456,0.0003165957,0.0001543372,0.0001060834,0.00914875,0.0007675464,0.001644489,0.9589305,0.007705568],"study_design_scores_gemma":[0.001781378,0.0001562194,0.0642046,0.0005679477,0.0003033029,0.000421147,0.0003775254,0.01910705,0.001657672,0.01035546,0.9008902,0.0001775495],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004900898,0.00032381,0.0004620057,0.0004299927,0.000117418,0.00001933869,0.989659,0.001197263,0.002890248],"genre_scores_gemma":[0.009654867,0.00009543912,0.001213174,0.0001989869,0.00001635785,0.00006644728,0.98774,0.0001645477,0.0008502896],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03069505,"threshold_uncertainty_score":0.09392148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05863635246616979,"score_gpt":0.2906266457018117,"score_spread":0.2319902932356419,"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."}}