{"id":"W4316170083","doi":"10.5194/isprs-annals-x-4-w1-2022-515-2023","title":"WHEAT BIOMASS ESTIMATION FROM UAV IMAGERY USING AN ENSEMBLE LEARNING APPROACH WITH BAYESIAN OPTIMIZATION","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Random forest; Feature selection; Mean squared error; Boosting (machine learning); Biomass (ecology); Gradient boosting; Regression; Computer science; Bayesian probability; Environmental science; Machine learning; Statistics; Agricultural engineering; Mathematics; Artificial intelligence; Agronomy; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009288383,0.000692949,0.0008035578,0.000785472,0.0002405846,0.0004466744,0.0004681442,0.0005354658,0.000484743],"category_scores_gemma":[0.00135705,0.0003656237,0.0008014957,0.0005997753,0.0001527041,0.0004650483,0.0003446254,0.0005163414,0.0001893578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002711838,"about_ca_system_score_gemma":0.0004037844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006223834,"about_ca_topic_score_gemma":0.005456287,"domain_scores_codex":[0.9997805,0.00007814074,0.00001375136,0.00005099633,0.00004876775,0.00002790778],"domain_scores_gemma":[0.9995635,0.0001913159,0.00005101366,0.00003540215,0.0001415435,0.00001716551],"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":[0.0001305574,0.00009818246,0.004985532,0.00004518148,0.0001259432,0.00004958114,0.00003887743,0.8233259,0.006866059,0.0005957793,0.0006544355,0.1630839],"study_design_scores_gemma":[0.000001819933,0.000009525096,0.0005161026,0.000001908907,0.000006568108,0.000003723165,0.000002531854,0.998923,0.0003620397,0.0001183305,0.00005225226,0.000002107011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1907809,0.000548012,0.8063477,0.0001434977,0.00004700608,0.00003469004,0.000111439,0.0005688292,0.001418032],"genre_scores_gemma":[0.8659866,0.0002113064,0.1326055,0.00004996907,0.00002824647,0.00004078018,0.0002550048,0.00003299848,0.0007895303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006223834,"threshold_uncertainty_score":0.01237518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03452253242453115,"score_gpt":0.2723166122396803,"score_spread":0.2377940798151492,"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."}}