{"id":"W6894267520","doi":"10.5683/sp2/ezo3ip","title":"Data from: Evolution of invasiveness by genetic accommodation","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adaptation (eye); Adaptive evolution; Phenotype; Genetic variation; Selection (genetic algorithm); Invasive species; Sunflower; Accommodation","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":[0.001765735,0.002325277,0.002126456,0.00369682,0.001291221,0.002773433,0.002746422,0.002713577,0.05273829],"category_scores_gemma":[0.008229354,0.0006715581,0.001606354,0.005676628,0.0006224844,0.001238212,0.002203912,0.001871141,0.05223083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128163,"about_ca_system_score_gemma":0.002207909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02018735,"about_ca_topic_score_gemma":0.03824167,"domain_scores_codex":[0.9985837,0.000282525,0.0001946191,0.0005030218,0.0002678627,0.0001682886],"domain_scores_gemma":[0.9967461,0.001610042,0.0003877537,0.0004783942,0.0005431356,0.0002346217],"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":[0.000474281,0.00008229147,0.009734953,0.008018575,0.0006350486,0.0002487851,0.0001835992,0.001428925,0.001112213,0.001498103,0.9696945,0.006888735],"study_design_scores_gemma":[0.000825141,0.00004708625,0.01795598,0.00100572,0.0003073196,0.0002304828,0.0001613094,0.000806438,0.0006892568,0.001663582,0.9762328,0.00007483963],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005529654,0.000359625,0.00009457574,0.00007185878,0.00002741042,0.000009508576,0.9980907,0.0002237236,0.0005695228],"genre_scores_gemma":[0.00166363,0.0001708922,0.0003829189,0.00005065853,0.000007428083,0.0000732999,0.9971145,0.00007993623,0.0004566925],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05273829,"threshold_uncertainty_score":0.1764272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05255563985323754,"score_gpt":0.2995584512568619,"score_spread":0.2470028114036244,"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."}}