{"id":"W2105101734","doi":"10.1016/j.compag.2008.07.008","title":"Predict soil texture distributions using an artificial neural network model","year":2008,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":210,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of New Brunswick","funders":"Agriculture and Agri-Food Canada","keywords":"Silt; Soil texture; Environmental science; Artificial neural network; Digital elevation model; Watershed; Soil science; Hydrology (agriculture); Terrain; Gradation; Soil map; Remote sensing; Soil water; Geology; Geotechnical engineering; Artificial intelligence; Machine learning; Computer science; Geography; Cartography; Geomorphology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00003750282,0.0001788445,0.0001624463,0.00002649533,0.0001974636,0.00004308147,0.0001082148,0.000178545,9.734445e-7],"category_scores_gemma":[0.000001856001,0.0001380122,0.00003988631,0.000300418,0.00003092077,0.0001453451,0.00002294602,0.0004924394,5.37973e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008687918,"about_ca_system_score_gemma":0.00002498947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009401898,"about_ca_topic_score_gemma":0.0001198309,"domain_scores_codex":[0.9991143,0.00001664447,0.0001549583,0.0001923157,0.00008759967,0.0004341553],"domain_scores_gemma":[0.9997495,0.00001145474,0.00001796365,0.0001033226,0.00002640986,0.00009129862],"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":[0.000005114747,0.00002609259,0.0002207248,0.00000640823,0.00001387629,0.00001137581,0.0001705066,0.9901497,0.001117001,0.0008846507,0.00596545,0.001429107],"study_design_scores_gemma":[0.0001426973,0.00003428318,0.001162493,0.00001655414,0.00001137302,0.0001025591,0.0000128527,0.9968388,0.0001494393,0.0007739181,0.0005565737,0.0001984203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625317,0.002741124,0.03397033,0.00004456389,0.0002988801,0.00009271348,0.00001566994,0.0001981856,0.0001067743],"genre_scores_gemma":[0.9981684,0.0003773588,0.0007790226,0.00004777583,0.0004732491,0.000004308233,0.0001210532,0.00001312658,0.00001566967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03563668,"threshold_uncertainty_score":0.5627974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375402248161804,"score_gpt":0.1966515384543626,"score_spread":0.1828975159727445,"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."}}