{"id":"W4386492988","doi":"10.5194/essd-15-3963-2023","title":"CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada","year":2023,"lang":"en","type":"article","venue":"Earth system science data","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Food and Agriculture","keywords":"Metadata; Computer science; Database; Environmental data; Analytics; Data mining; Consistency (knowledge bases); Pipeline (software); Big data; Data quality; Data science; Biology; Ecology; Artificial intelligence","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":[0.001052853,0.0008461705,0.0005223094,0.004299852,0.001771508,0.002309906,0.001873182,0.0005628877,0.004389783],"category_scores_gemma":[0.003673385,0.0004027293,0.0006678149,0.009476703,0.0004843973,0.001212349,0.001648288,0.00075418,0.001912557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01160245,"about_ca_system_score_gemma":0.02709195,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9258906,"about_ca_topic_score_gemma":0.9335763,"domain_scores_codex":[0.9992062,0.00004473676,0.00006024533,0.0002194187,0.0003447979,0.0001244811],"domain_scores_gemma":[0.9970096,0.0001990983,0.0002271696,0.0003887353,0.001713828,0.0004615069],"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":[0.001631255,0.0002233335,0.1616577,0.001571818,0.0008976272,0.0008763366,0.001622163,0.02557028,0.02387193,0.0225356,0.6392765,0.1202655],"study_design_scores_gemma":[0.0002737725,0.00004973697,0.2830285,0.0003932292,0.0002631959,0.0002124065,0.001530255,0.0362432,0.01208602,0.004707396,0.6608607,0.0003515982],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03369481,0.0005641482,0.005369924,0.0003782994,0.00003213216,0.000163783,0.9460161,0.006039266,0.007741456],"genre_scores_gemma":[0.07029828,0.0007242158,0.01622307,0.0001702194,0.000009974333,0.0001843448,0.9097317,0.0006189668,0.002039272],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07410938,"threshold_uncertainty_score":0.1490916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04317721726563633,"score_gpt":0.2687151645684801,"score_spread":0.2255379473028438,"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."}}