{"id":"W4393551734","doi":"10.5281/zenodo.6299090","title":"Large-scale and Multi-dimensional Climate, Genetics, and Phenotypes Database for Maize Yield Predictability in the U.S. and Canada","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Predictability; Yield (engineering); Scale (ratio); Phenotype; Biology; Geography; Genetics; Statistics; Mathematics; Gene; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000742533,0.0001139509,0.000118505,0.00001851423,0.0013807,0.000252463,0.0003624598,0.00005210204,0.005105563],"category_scores_gemma":[0.0002157649,0.00005664447,0.00001214939,0.00009807324,0.00006878776,0.00003417687,0.001092493,0.0002115447,0.000004322977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002280446,"about_ca_system_score_gemma":0.000003551129,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01851016,"about_ca_topic_score_gemma":0.0556397,"domain_scores_codex":[0.9988849,0.0001510104,0.0001509068,0.0003551906,0.0002129805,0.0002450264],"domain_scores_gemma":[0.9995318,0.0001574094,0.00005872356,0.00009653419,0.00007006869,0.0000854407],"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":[0.00003891502,0.0000562334,0.0001294177,0.00008358363,0.000007254048,0.00000472356,0.00008919935,0.000003059567,0.0006581555,0.00001038564,0.9950307,0.003888377],"study_design_scores_gemma":[0.0001679149,0.0001393303,0.008938406,0.00001446492,0.00001339289,0.00003554151,0.0004620488,0.0002531037,0.000005780496,0.000009021499,0.989849,0.0001119472],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1183766,0.0003957339,0.000001499142,0.0004244804,0.0000473013,0.0003988286,0.8802947,0.00001639735,0.00004441845],"genre_scores_gemma":[0.02047496,0.001179942,0.0000697334,0.0002724732,0.00008864467,2.5259e-7,0.9778692,0.00002608932,0.00001874486],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09790161,"threshold_uncertainty_score":0.9999194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0401259440993038,"score_gpt":0.2162861125872456,"score_spread":0.1761601684879419,"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."}}