{"id":"W3203397947","doi":"10.1139/cjss-2021-0034","title":"Factors affecting the use of weather station data in predicting surface soil moisture for agricultural applications","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Weather station; Water content; Soil texture; Growing season; Automatic weather station; Cover crop; Hydrology (agriculture); Agriculture; Crop residue; Moisture; Agronomy; Soil water; Soil science; Meteorology; Geography; Agroforestry; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003710627,0.0003177338,0.0002725509,0.0003921195,0.0002559791,0.0009058521,0.0002565721,0.0002744233,0.0002687748],"category_scores_gemma":[0.0125677,0.0002352544,0.0002245063,0.0009395893,0.0002089871,0.0005552399,0.0002491632,0.0003537824,0.0001610337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003541228,"about_ca_system_score_gemma":0.0003501744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01937991,"about_ca_topic_score_gemma":0.02983865,"domain_scores_codex":[0.9980572,0.0008212992,0.0002717044,0.0003616663,0.0003816523,0.0001064219],"domain_scores_gemma":[0.9868554,0.008428259,0.002510197,0.000746107,0.001282725,0.0001771746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006027193,0.00001998757,0.9868279,0.00002688272,0.0000693461,0.00004675976,0.000104284,0.004319984,0.001941435,0.00002823806,0.0001107533,0.006444348],"study_design_scores_gemma":[0.000002427853,0.00004180523,0.9843743,0.00001245614,0.00001940053,0.00004037502,0.0001278204,0.01381855,0.001127667,0.00002979414,0.0003969015,0.00000848821],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960299,0.0001871243,0.002603492,0.00004547575,0.000009822278,0.00001720328,0.0004269907,0.00002869195,0.0006513135],"genre_scores_gemma":[0.9979684,0.00005537531,0.001461315,0.00001056745,0.000004664756,0.000006393569,0.0003973264,0.00000707985,0.00008886547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01937991,"threshold_uncertainty_score":0.03853422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04612506005451963,"score_gpt":0.251275830530358,"score_spread":0.2051507704758383,"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."}}