{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001070315,0.0006859119,0.0003127738,0.0008446633,0.0004316848,0.0008099782,0.001154455,0.0004575597,0.001616108],"category_scores_gemma":[0.002767698,0.0002277927,0.0004124366,0.001063434,0.0002593033,0.0003726007,0.0003146377,0.0004642108,0.0002992529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0096195,"about_ca_system_score_gemma":0.00389537,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.967263,"about_ca_topic_score_gemma":0.9497728,"domain_scores_codex":[0.9997719,0.00005756771,0.00001171181,0.00007115215,0.00004330579,0.00004446507],"domain_scores_gemma":[0.9984547,0.0006197751,0.0001413301,0.00007311359,0.0006576779,0.00005344382],"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":[0.0003187133,0.0001585744,0.2112646,0.0000884227,0.0002831675,0.0001181316,0.00010234,0.7296339,0.00175155,0.0008282686,0.004363762,0.05108855],"study_design_scores_gemma":[0.00001164968,0.00001585661,0.04953043,0.0000110656,0.0000143284,0.000005082826,0.00003746645,0.9493486,0.0002508546,0.000155946,0.000607866,0.00001085714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667641,0.0004669041,0.02283967,0.0003433549,0.00001955846,0.00009851418,0.006077916,0.0004306991,0.002959168],"genre_scores_gemma":[0.98673,0.0001231381,0.00666894,0.00004466846,0.000005240055,0.00004797997,0.00401324,0.0000209577,0.002345759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03273696,"threshold_uncertainty_score":0.06979471,"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."}}