{"id":"W4393437276","doi":"10.5281/zenodo.8002909","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; Omics; Database; Computer science; Data science; Computational biology; 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":[0.001149267,0.00169601,0.0008402666,0.002845202,0.001018037,0.002711854,0.002091462,0.0008934545,0.01889151],"category_scores_gemma":[0.003614299,0.0007065921,0.001667134,0.006019786,0.0003069525,0.00183042,0.002301604,0.001262737,0.01779838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002009544,"about_ca_system_score_gemma":0.003142661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06274695,"about_ca_topic_score_gemma":0.07715096,"domain_scores_codex":[0.9991828,0.00006732208,0.00007077391,0.000293806,0.0002573145,0.0001281072],"domain_scores_gemma":[0.9988024,0.000119085,0.0001193505,0.0003337642,0.0004688505,0.0001564745],"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":[0.0004971807,0.00006643715,0.01605296,0.001100127,0.0003375006,0.0002004304,0.0003371606,0.004094874,0.00824447,0.005914907,0.9372199,0.02593411],"study_design_scores_gemma":[0.0002314507,0.00003782292,0.03089588,0.0002107152,0.0001318806,0.0001397331,0.000300286,0.008700706,0.007307884,0.008364028,0.9435405,0.0001391067],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001879035,0.0001973062,0.002797229,0.0002310107,0.00004668211,0.00005904427,0.9809291,0.01139444,0.002466165],"genre_scores_gemma":[0.006204505,0.0002337408,0.00782159,0.000226181,0.00002038477,0.0001636547,0.9829803,0.00148697,0.0008627794],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06274695,"threshold_uncertainty_score":0.1247634,"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."}}