{"id":"W7025439047","doi":"","title":"Using UAVs and NDVI Readings to Predict Grower N Rates in North Carolina Cotton","year":2022,"lang":"en","type":"report","venue":"VTechWorks (Virginia Tech)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lint; Normalized Difference Vegetation Index; Sowing; Vegetation (pathology); Plot (graphics); Hydrology (agriculture)","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":[0.0001819102,0.0003290197,0.0001693315,0.000705577,0.0003190399,0.000399344,0.0003303152,0.0001829944,0.0004274505],"category_scores_gemma":[0.0005962118,0.0001799187,0.0001678199,0.0003833473,0.00009447576,0.0002089404,0.0001847178,0.0001636447,0.0001252479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008545104,"about_ca_system_score_gemma":0.0005281921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2115122,"about_ca_topic_score_gemma":0.3550561,"domain_scores_codex":[0.9998714,0.00001596788,0.00000556995,0.00004626099,0.0000393343,0.00002148111],"domain_scores_gemma":[0.9995871,0.0001521643,0.00006179562,0.00001724984,0.0001388375,0.00004280106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005476863,0.0003812416,0.8386334,0.00008162452,0.00008954677,0.0007596932,0.0005463237,0.03190823,0.0661539,0.0000745947,0.0007886349,0.06003525],"study_design_scores_gemma":[0.00001223643,0.0001985647,0.8334872,0.00001656053,0.0000283854,0.0001141051,0.0008132757,0.1547936,0.009732628,0.00002904508,0.0007506522,0.00002366436],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987715,0.00002633617,0.0005769967,0.00001007667,0.000001797553,0.00001209969,0.000138379,0.00004135567,0.0004214548],"genre_scores_gemma":[0.9963844,0.00004800001,0.002584407,0.000007770173,0.00000126227,0.0000129832,0.0003659743,0.000006021833,0.0005891832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2115122,"threshold_uncertainty_score":0.420562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03853927369517746,"score_gpt":0.3192458091369338,"score_spread":0.2807065354417564,"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."}}