{"id":"W4292014083","doi":"10.1002/saj2.20469","title":"Machine learning models for predicting soil particle size fractions from routine soil analyses in Quebec","year":2022,"lang":"en","type":"article","venue":"Soil Science Society of America Journal","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de Recherche et de Développement en Agroenvironnement","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Soil texture; Silt; Soil science; Particle size; Soil test; Cation-exchange capacity; Particle-size distribution; Linear regression; USDA soil taxonomy; Random forest; Soil water; Bulk density; Particle (ecology); Soil type; Soil classification; Environmental science; Mathematics; Statistics; Machine learning; Geology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001282527,0.0001437352,0.0002549438,0.00003995801,0.001700029,0.0001137944,0.000486313,0.00002671397,0.0008160466],"category_scores_gemma":[0.0004111504,0.0001410361,0.0002125097,0.001209003,0.0007928518,0.0006662307,0.000422959,0.0005751737,0.00000486459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005500483,"about_ca_system_score_gemma":0.0002220991,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1053901,"about_ca_topic_score_gemma":0.002619456,"domain_scores_codex":[0.9975386,0.0001031338,0.0004725494,0.0003803562,0.0009375859,0.0005677998],"domain_scores_gemma":[0.99866,0.0004525681,0.0004864188,0.0001650305,0.00004697198,0.0001890613],"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":[0.00001879055,0.0001172678,0.06795762,0.000003139535,0.00002175283,0.000001747289,0.006376043,0.8828648,0.0296706,0.00001227067,0.0006026027,0.01235341],"study_design_scores_gemma":[0.0005490822,0.0001324398,0.02529156,0.00001284774,0.00002837352,0.000009085862,0.0162721,0.9535051,0.0008610394,0.002260006,0.0009097075,0.0001686532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565391,0.0001458184,0.04087315,0.001210393,0.0001992794,0.0001208142,0.00005666216,0.0000297931,0.0008249833],"genre_scores_gemma":[0.984256,0.0001220192,0.01487027,0.0003348751,0.00006316951,0.00002269938,0.000005931361,0.00001353918,0.0003114954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1027706,"threshold_uncertainty_score":0.9995996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02917052621211144,"score_gpt":0.2863735929753251,"score_spread":0.2572030667632136,"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."}}