{"id":"W6929776487","doi":"10.5061/dryad.8n2d374","title":"Data from: Genomic selection of juvenile height across a single generational gap in Douglas-fir","year":2018,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Cancer Research and Treatments","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Regression; Population; Genomic selection; Linear regression; Regression analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001151231,0.000232703,0.0002143485,0.0005261628,0.0004210142,0.0003017419,0.0003207658,0.0002640923,0.001183035],"category_scores_gemma":[0.001334252,0.0001036715,0.0004001914,0.0006232644,0.0002257035,0.0001206717,0.0003327616,0.0003433615,0.0002852663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005038353,"about_ca_system_score_gemma":0.0003594552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04481391,"about_ca_topic_score_gemma":0.1112773,"domain_scores_codex":[0.9996628,0.00009944916,0.00001551695,0.0001256501,0.0000586627,0.0000380221],"domain_scores_gemma":[0.9988915,0.0004111652,0.0001108051,0.0002954265,0.000199834,0.0000911872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000857899,0.0002380714,0.8936821,0.0001380127,0.0006156798,0.0004869936,0.0007617766,0.0124126,0.04692969,0.0004640656,0.003578618,0.0398345],"study_design_scores_gemma":[0.00001712529,0.00008550617,0.9908133,0.000007235037,0.00006922316,0.0001835714,0.0001569965,0.003002508,0.002631347,0.0001288368,0.002886987,0.00001743249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9894947,0.00005441039,0.002168381,0.00002014806,0.000005496859,0.00001278337,0.007608636,0.00009713374,0.0005382036],"genre_scores_gemma":[0.9735178,0.00002834881,0.004542732,0.0000376779,0.00000335334,0.00002773694,0.02122707,0.00003799356,0.0005773704],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.04481391,"threshold_uncertainty_score":0.08910608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04385736713944192,"score_gpt":0.3230024366669064,"score_spread":0.2791450695274645,"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."}}