{"id":"W4322010548","doi":"10.5194/egusphere-egu23-8637","title":"Selection of NPK specific spectral bands using Hyperspectral imagery and ensemble machine learning approach over agricultural lands in Morocco","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Hyperspectral imaging; VNIR; Partial least squares regression; Remote sensing; Context (archaeology); Principal component analysis; Spectral bands; Environmental science; Collinearity; Kriging; Feature selection; Computer science; Mathematics; Artificial intelligence; Machine learning; Statistics; Geography","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":[0.0005614916,0.0004859197,0.0003331527,0.001156629,0.0003209644,0.0004769219,0.00037724,0.0002832988,0.0002737661],"category_scores_gemma":[0.0004927161,0.0001119636,0.0004305893,0.0007338508,0.0001945906,0.0002674933,0.0002711236,0.000164174,0.00008534519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007342328,"about_ca_system_score_gemma":0.000346299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05829579,"about_ca_topic_score_gemma":0.07081404,"domain_scores_codex":[0.999826,0.00004311078,0.000009716607,0.00005329969,0.00002786018,0.00003993169],"domain_scores_gemma":[0.9998403,0.00004295455,0.00002732934,0.00001548777,0.00006018463,0.00001377521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008294908,0.000457825,0.3509572,0.0003423546,0.0005118868,0.001583874,0.0009365396,0.2490527,0.06303654,0.0006739771,0.002275966,0.3293417],"study_design_scores_gemma":[0.00001499406,0.00006601515,0.4133888,0.00002922515,0.0001360649,0.00009874556,0.0007881063,0.5778971,0.006282343,0.0002013459,0.00106457,0.00003274426],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997063,0.0002183467,0.002023782,0.00007183806,0.000007836076,0.000007006156,0.0001802906,0.00003886669,0.0003890758],"genre_scores_gemma":[0.9964566,0.0000832989,0.00288062,0.00001238542,0.000007232438,0.000005285494,0.0003295758,0.000005042035,0.0002200365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05829579,"threshold_uncertainty_score":0.1159129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02499401394600023,"score_gpt":0.2353912000281668,"score_spread":0.2103971860821666,"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."}}