{"id":"W2756912109","doi":"10.3390/app7100975","title":"Assessing the Performance of Thermal Inertia and Hydrus Models to Estimate Surface Soil Water Content","year":2017,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Soil and Unsaturated Flow","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Università di Pisa","keywords":"Environmental science; Mean squared error; Soil water; Water content; Soil science; Soil thermal properties; Hydrology (agriculture); Field capacity; Mathematics; Geology; Geotechnical engineering; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001060141,0.0009044002,0.0005121093,0.0005820096,0.0002314376,0.0005275498,0.0006936091,0.0007061145,0.0003369387],"category_scores_gemma":[0.002044759,0.0003239642,0.0005098716,0.0003309847,0.0002851404,0.0007136107,0.0006181511,0.0003612517,0.0001304553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003628771,"about_ca_system_score_gemma":0.0005615952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01040176,"about_ca_topic_score_gemma":0.006117114,"domain_scores_codex":[0.9997067,0.00008120854,0.00002041192,0.00009022625,0.00007005275,0.0000314237],"domain_scores_gemma":[0.9990681,0.000578303,0.0001032598,0.00009143409,0.0001179343,0.00004106325],"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":[0.0003958385,0.0001634129,0.03764454,0.000148943,0.0002013266,0.00006869314,0.0001276616,0.9042988,0.01833588,0.0004899383,0.0001952051,0.03792975],"study_design_scores_gemma":[0.00001213754,0.00007539333,0.006156836,0.000004976425,0.00001792324,0.00001316895,0.0000219955,0.9894734,0.003998855,0.0001063098,0.0001024682,0.00001664248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9402401,0.0003338377,0.057225,0.00007631299,0.00001889753,0.00003521007,0.0002486274,0.0005198129,0.001302071],"genre_scores_gemma":[0.9894797,0.0001013415,0.009932917,0.00001045607,0.000005527812,0.00003129283,0.0001759018,0.00002034097,0.0002423463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01040176,"threshold_uncertainty_score":0.02068239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04971223878992745,"score_gpt":0.2672509181446678,"score_spread":0.2175386793547404,"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."}}