{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000608449,0.001136841,0.0006687734,0.003274016,0.0016352,0.00131852,0.002104592,0.000651024,0.004887307],"category_scores_gemma":[0.002760779,0.0003967011,0.001235454,0.009109298,0.000472957,0.0005776665,0.001274726,0.0009443757,0.002968662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0127884,"about_ca_system_score_gemma":0.02489451,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9693801,"about_ca_topic_score_gemma":0.9802573,"domain_scores_codex":[0.9991001,0.00004240416,0.0000576988,0.0002386812,0.0003655967,0.0001955755],"domain_scores_gemma":[0.9970574,0.0001687912,0.0001747998,0.0003036884,0.001959746,0.0003355045],"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":[0.0005051306,0.0001567141,0.10961,0.001051553,0.0005702293,0.0004036354,0.0003916603,0.01071817,0.003640588,0.004083937,0.8337753,0.03509313],"study_design_scores_gemma":[0.0002156584,0.00004808204,0.3628758,0.0004493202,0.0002463516,0.0001774553,0.0009295066,0.01437808,0.003139977,0.001827776,0.6154599,0.0002521393],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008249516,0.0002325798,0.000597978,0.0001280586,0.00001536068,0.00003219363,0.9885821,0.0005302047,0.001632066],"genre_scores_gemma":[0.01087351,0.0001960238,0.001367922,0.00006579996,0.000003917383,0.0000508454,0.9865876,0.00006400077,0.0007903519],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03061992,"threshold_uncertainty_score":0.09278679,"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."}}