{"id":"W6911252421","doi":"10.5061/dryad.vk048","title":"Data from: Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform","year":2017,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"","keywords":"Predictive modelling; Regression; Exome; Heritability; Genotyping; Linear regression; Range (aeronautics); Selection (genetic algorithm); Tree (set theory)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001018666,0.0004489832,0.0003433538,0.0005371289,0.0003714373,0.0006221163,0.0004348487,0.0003910371,0.002550689],"category_scores_gemma":[0.00221757,0.0001897981,0.0004405168,0.000714482,0.0001953878,0.0002012343,0.0004463856,0.0004422659,0.001146956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008432222,"about_ca_system_score_gemma":0.0006746346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1318811,"about_ca_topic_score_gemma":0.2852809,"domain_scores_codex":[0.999608,0.00006725825,0.00001942711,0.0001716153,0.00007995725,0.00005375843],"domain_scores_gemma":[0.9984363,0.0005932625,0.0001656917,0.0002713031,0.0004536448,0.00007983269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001331559,0.0001621116,0.818801,0.0004242065,0.0009786133,0.0003530148,0.000605921,0.0352186,0.03672352,0.0005839419,0.01741914,0.08739831],"study_design_scores_gemma":[0.00005100945,0.00007442337,0.9579228,0.0000536147,0.0002070901,0.0002046234,0.0002337425,0.0217013,0.007719557,0.0004258163,0.01135372,0.00005232629],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.8927642,0.0003563216,0.01278716,0.0001202932,0.00002075464,0.00004297326,0.0911826,0.0006170185,0.002108638],"genre_scores_gemma":[0.8107584,0.0001403163,0.01362383,0.000113888,0.000009423899,0.000101924,0.1722897,0.0001463768,0.00281611],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1318811,"threshold_uncertainty_score":0.2622269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05592353086013595,"score_gpt":0.3012364551348942,"score_spread":0.2453129242747582,"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."}}