{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001910451,0.0004763694,0.0003263794,0.0004620036,0.0002321727,0.0004061175,0.0004386413,0.0007680766,0.0008659372],"category_scores_gemma":[0.0008404524,0.0003567224,0.0003240989,0.0004791383,0.0002217758,0.0006276117,0.0002121219,0.0004368407,0.0001773481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005930614,"about_ca_system_score_gemma":0.0004151634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01614209,"about_ca_topic_score_gemma":0.01439199,"domain_scores_codex":[0.9999529,0.000006532405,0.000002760544,0.00001806654,0.00001073627,0.000009072372],"domain_scores_gemma":[0.9996614,0.0001947095,0.0000319157,0.00001360215,0.00008230709,0.00001599377],"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":[0.00006537911,0.00005623082,0.003310998,0.00001165637,0.00001733121,0.00002631573,0.000006182627,0.9808093,0.001587092,0.0001663073,0.0001421112,0.01380111],"study_design_scores_gemma":[0.000001632577,0.000002016163,0.0001788862,2.841479e-7,0.000001022584,7.772475e-7,5.887744e-7,0.9996375,0.000119334,0.0000512031,0.000006059358,6.181977e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7740877,0.0002084582,0.2216315,0.0001996229,0.0001036711,0.00004189475,0.0003125561,0.0006787248,0.00273593],"genre_scores_gemma":[0.9855318,0.0000672956,0.01317471,0.00002166739,0.0000143488,0.00002132089,0.0001334933,0.00001373711,0.001021668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01614209,"threshold_uncertainty_score":0.03209627,"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."}}