{"id":"W2039166212","doi":"10.1097/ss.0b013e318241119a","title":"Fractal Description of the Spatial and Temporal Variability of Soil Water Content Across an Agricultural Field","year":2012,"lang":"en","type":"article","venue":"Soil Science","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Fractal; Fractal dimension; Soil science; Spatial variability; Water content; Mathematics; Loam; Soil water; Topsoil; Fractional Brownian motion; Sampling (signal processing); Environmental science; Hydrology (agriculture); Statistics; Geology; Brownian motion; Physics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004023072,0.0001124952,0.0002005009,0.001389449,0.0001621568,0.0003431712,0.0002496128,0.0001526303,0.0002431957],"category_scores_gemma":[0.001428317,0.0001222627,0.0003172867,0.000583776,0.0004433767,0.0003373861,0.0001342109,0.0001688946,0.0000298501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005321801,"about_ca_system_score_gemma":0.0002059096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008681131,"about_ca_topic_score_gemma":0.005113761,"domain_scores_codex":[0.9999067,0.00001545457,0.000006259042,0.00002648218,0.00002328663,0.00002183824],"domain_scores_gemma":[0.999193,0.0003750965,0.0001960185,0.00008602284,0.0001098926,0.000039888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002037196,0.00008446933,0.2891031,0.0002364656,0.0003465254,0.0009380247,0.001106639,0.5046465,0.09019203,0.03303373,0.001665783,0.07844307],"study_design_scores_gemma":[0.000006996289,0.00004609359,0.2623188,0.00001364525,0.00003192822,0.0003040904,0.0001029469,0.7240222,0.001180411,0.01097162,0.0009629959,0.00003830155],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463018,0.0003546434,0.05205529,0.0000676268,0.000007792692,0.00001203026,0.0002833845,0.000072321,0.0008450679],"genre_scores_gemma":[0.99686,0.0000833598,0.002704156,0.000005884427,0.000007966506,0.0000100175,0.0001659141,0.000006849922,0.0001557986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008681131,"threshold_uncertainty_score":0.01726121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061214308413681,"score_gpt":0.232945013195563,"score_spread":0.2123328701114262,"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."}}