{"id":"W4394046296","doi":"10.5281/zenodo.7490246","title":"CLImate for Maize OMICS: CLIM4OMICS Analytics and Database","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Crop Yield and Soil Fertility","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; Database; Omics; Data science; Computer science; Geography; Biology; Bioinformatics","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":[0.001114267,0.001720794,0.0008378293,0.002783223,0.001034832,0.002686097,0.002104328,0.0008782265,0.01852817],"category_scores_gemma":[0.003461087,0.0007151551,0.00167604,0.005754471,0.0003097951,0.001784952,0.002348449,0.001283602,0.01806715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001947052,"about_ca_system_score_gemma":0.003081334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0599118,"about_ca_topic_score_gemma":0.07563033,"domain_scores_codex":[0.9991727,0.00006661932,0.0000692401,0.0003048195,0.0002564567,0.0001301746],"domain_scores_gemma":[0.9988808,0.0001106295,0.0001115705,0.0003163131,0.0004348936,0.0001457652],"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":[0.0005203002,0.00007083263,0.01649197,0.001150714,0.0003551433,0.0002082216,0.0003736496,0.003956192,0.009474024,0.005803679,0.9354154,0.02617981],"study_design_scores_gemma":[0.0002367437,0.0000389074,0.03201131,0.0002098311,0.0001350922,0.0001423555,0.0003077519,0.00804941,0.00788489,0.00795287,0.9428906,0.0001401975],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002016928,0.0001977167,0.002740318,0.0002182316,0.00004684862,0.00005948173,0.9806647,0.01162914,0.002426518],"genre_scores_gemma":[0.006043144,0.0002192719,0.007530719,0.0002170052,0.00001930477,0.0001631738,0.9834338,0.001515583,0.0008580733],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0599118,"threshold_uncertainty_score":0.1191261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0649762199690221,"score_gpt":0.2609719268026672,"score_spread":0.1959957068336451,"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."}}