{"id":"W4391583894","doi":"10.1109/icdmw60847.2023.00111","title":"Using UAV-Based Multispectral Imagery, Data-Driven Models, and Spatial Cross-Validation for Corn Grain Yield Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Multispectral image; Remote sensing; Yield (engineering); Computer science; Grain yield; Data modeling; Cross-validation; Predictive modelling; Artificial intelligence; Environmental science; Geography; Machine learning; Materials science; Agronomy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002489439,0.001279638,0.0004904725,0.0007624017,0.0003064917,0.0006877466,0.0008934558,0.0006957203,0.0006478949],"category_scores_gemma":[0.00465221,0.0004191491,0.0007790991,0.000821748,0.0003002282,0.001078889,0.0006031504,0.001094513,0.000257454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000942956,"about_ca_system_score_gemma":0.0009029842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03547229,"about_ca_topic_score_gemma":0.04343969,"domain_scores_codex":[0.999422,0.0001914445,0.00004519929,0.0001796492,0.00009781079,0.00006396372],"domain_scores_gemma":[0.9985358,0.0007387399,0.0001584718,0.0001870016,0.0003407829,0.00003928375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001013907,0.0001350056,0.01117238,0.00005922264,0.0001560629,0.00005291558,0.00002231813,0.9384769,0.002204018,0.0003670481,0.0009242747,0.04632837],"study_design_scores_gemma":[0.000003719007,0.00001966751,0.001907423,0.000006500722,0.000009718044,0.000005951709,0.000007974823,0.9968109,0.0008660635,0.000231124,0.0001265119,0.000004447988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6609827,0.001546608,0.3299751,0.0004152059,0.0001164853,0.00009650499,0.001213001,0.002838102,0.002816299],"genre_scores_gemma":[0.9489001,0.0001598788,0.04856806,0.0001041704,0.00001473198,0.00005568462,0.001464483,0.00006256206,0.0006703351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03547229,"threshold_uncertainty_score":0.07053161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08547860790100742,"score_gpt":0.3084914844689861,"score_spread":0.2230128765679787,"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."}}