{"id":"W4414301106","doi":"10.1007/s11119-025-10283-9","title":"Reducing corn yield prediction uncertainty through multi-scale integration of ground, drone, and satellite data","year":2025,"lang":"en","type":"article","venue":"Precision Agriculture","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Precision agriculture; Satellite; Propagation of uncertainty; Spatial variability; Field (mathematics); Accuracy and precision; Crop yield; Temporal scales; Yield (engineering)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003251535,0.0002200996,0.0002395811,0.00002782561,0.0001755547,0.00006948174,0.0004021737,0.0002597877,0.00006696463],"category_scores_gemma":[0.0002925096,0.0001303707,0.00004447055,0.0007058189,0.0001403569,0.0005918844,0.0004344784,0.0003098643,0.00002512673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001295772,"about_ca_system_score_gemma":0.000008803848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050158,"about_ca_topic_score_gemma":0.0007636281,"domain_scores_codex":[0.9982455,0.0000997153,0.0004083183,0.0006741504,0.0003734116,0.0001988729],"domain_scores_gemma":[0.9989649,0.00013785,0.0001832211,0.0005915627,0.00006299947,0.00005947957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009892827,0.0002794813,0.01073778,0.00004726843,0.00004442674,0.000002597432,0.003532111,0.005379663,0.7128994,0.00005713078,0.06197949,0.2049417],"study_design_scores_gemma":[0.0008689363,0.0001105133,0.8929933,0.0009256178,0.0001537146,0.00004579555,0.001944173,0.04235962,0.02577446,0.0005384231,0.03385896,0.0004265131],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9056814,0.001020031,0.07399698,0.001225848,0.001259125,0.001374586,0.000230893,0.0002272083,0.0149839],"genre_scores_gemma":[0.9565675,0.0007071273,0.03877099,0.0001909815,0.00008368883,0.000003654747,0.0003818803,0.000009919254,0.003284288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8822555,"threshold_uncertainty_score":0.5316363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933011744370872,"score_gpt":0.2666074362117929,"score_spread":0.2372773187680842,"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."}}