{"id":"W4404922775","doi":"10.1007/s41064-024-00323-w","title":"Spatial Prediction of Soil Attributes from PRISMA Hyperspectral Imagery Using Wrapper Feature Selection and Ensemble Modeling","year":2024,"lang":"en","type":"article","venue":"PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Agenzia Spaziale Italiana","keywords":"Hyperspectral imaging; Feature selection; Pattern recognition (psychology); Artificial intelligence; Computer science; Selection (genetic algorithm); Feature (linguistics); Ensemble learning; Remote sensing; Data mining; Geology","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":[0.0009725651,0.0001142903,0.0001634659,0.0002580211,0.0002627765,0.000264845,0.00006749764,0.0000590433,0.000007782799],"category_scores_gemma":[0.0001708274,0.00009848823,0.00004642162,0.0006323734,0.0002006188,0.000914307,0.00006487075,0.0002264982,0.000001558829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001052195,"about_ca_system_score_gemma":0.00007316528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0045329,"about_ca_topic_score_gemma":0.0001226533,"domain_scores_codex":[0.9986846,0.00002344408,0.0003943285,0.0001707822,0.0005077313,0.0002190617],"domain_scores_gemma":[0.9993923,0.00007096808,0.0002270056,0.00007130134,0.0001201359,0.0001182805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003841039,0.0000090162,0.001289771,0.00004860718,0.00002366462,0.000007082185,0.002370381,0.02771262,0.3150898,0.000004207227,0.00009535705,0.6533111],"study_design_scores_gemma":[0.0001697931,0.00006757787,0.00257077,0.0001669758,0.00004086258,0.0004092486,0.000698442,0.9797903,0.01524219,0.0006626347,0.00009174232,0.00008942175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5553727,0.0001371419,0.4440805,0.00005900369,0.000202608,0.00004077746,0.000006318642,0.00001070028,0.00009034139],"genre_scores_gemma":[0.948924,0.0001244799,0.05082466,0.00002835938,0.00008144295,1.656997e-8,0.000003123471,0.000005558808,0.000008344776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9520777,"threshold_uncertainty_score":0.6852419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713423854791501,"score_gpt":0.2395200371433538,"score_spread":0.2223857985954388,"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."}}