{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008345578,0.0001972525,0.0002453209,0.00004839478,0.001878253,0.0008012513,0.0008687785,0.0001683753,0.00160148],"category_scores_gemma":[0.0007739053,0.0001063608,0.00008631615,0.0003264945,0.0001382567,0.000110231,0.001612452,0.0002911548,0.002949087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000545924,"about_ca_system_score_gemma":0.000001622834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001187554,"about_ca_topic_score_gemma":0.00003736098,"domain_scores_codex":[0.9983398,0.0001382263,0.0002950626,0.0005631786,0.000225149,0.0004386008],"domain_scores_gemma":[0.9990233,0.0001455091,0.0001318113,0.0002421192,0.0002737483,0.0001835763],"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":[0.00008922926,0.000058308,0.000001268539,0.0001274458,0.00002135946,0.000005792754,0.00001790693,9.368806e-7,0.0007207769,0.00008079413,0.9694637,0.02941245],"study_design_scores_gemma":[0.0001766785,0.0001935162,0.0006298376,0.00003337354,0.00003997913,0.00001469397,0.0001357734,0.000146308,0.00002391509,0.00009708271,0.9982921,0.0002167334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005885519,0.00004923166,0.000008683885,0.0005515896,0.0001442767,0.0004640561,0.9922128,0.0003016375,0.0003822522],"genre_scores_gemma":[0.0007633883,0.001651263,0.00002975234,0.0001569649,0.0003533914,9.321687e-8,0.9968044,0.00007991269,0.0001608882],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02919571,"threshold_uncertainty_score":0.9994212,"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."}}