{"id":"W4317932495","doi":"10.3390/w15030473","title":"Spatial Interpolation of Soil Temperature and Water Content in the Land-Water Interface Using Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"National Research Council Canada","keywords":"Interpolation (computer graphics); Multivariate interpolation; Spline (mechanical); Artificial neural network; Radial basis function; Spline interpolation; Mean squared error; Soil science; Environmental science; Data point; Water content; Computer science; Algorithm; Remote sensing; Machine learning; Mathematics; Artificial intelligence; Geography; Statistics; Engineering; Bilinear interpolation; Geotechnical engineering; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.0004397086,0.0001014943,0.0001144956,0.00005191801,0.00009708981,0.00002592638,0.0001228787,0.00005339891,0.0003194672],"category_scores_gemma":[0.00000435268,0.00004218487,0.00002400667,0.00004419374,0.000185156,0.0001024252,0.0003419779,0.0001070079,0.0003155054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001589518,"about_ca_system_score_gemma":4.558169e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001103647,"about_ca_topic_score_gemma":0.000604549,"domain_scores_codex":[0.9991536,0.00007995901,0.0001994122,0.0001884886,0.0001134334,0.0002650807],"domain_scores_gemma":[0.999841,0.0000134187,0.00001384841,0.000113193,0.000004383297,0.00001410431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002331795,0.00009723147,0.1338251,0.00004518994,0.0000568734,0.00003995234,0.04980295,0.01270018,0.8010622,0.00006865333,0.0004060363,0.001662457],"study_design_scores_gemma":[0.0001858774,0.0001247998,0.01925489,0.00002909798,0.00003164279,0.000006959164,0.001734651,0.01683001,0.9554166,0.005345526,0.0008204988,0.000219453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961126,0.00000442385,0.0002997153,0.003023767,0.0001385025,0.0001593331,0.000001416083,0.00001532376,0.0002449374],"genre_scores_gemma":[0.9994726,0.000007301676,0.0000208117,0.0002596819,0.00002683778,0.000009508572,0.00001249478,0.000006005769,0.0001847862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1543544,"threshold_uncertainty_score":0.4055289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03905474725907875,"score_gpt":0.2510550600092056,"score_spread":0.2120003127501268,"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."}}