{"id":"W2801328227","doi":"10.3390/agronomy8050059","title":"Characterizing Spatial Variability in Soil Water Content for Precision Irrigation Management","year":2018,"lang":"en","type":"article","venue":"Agronomy","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Environmental science; Water content; Soil water; Spatial variability; Irrigation; Neutron probe; Soil science; Spatial ecology; Scale (ratio); Hydrology (agriculture); Geography; Mathematics; Agronomy; Statistics; Ecology; Geology; Cartography; Neutron","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":[0.0004506853,0.0002115442,0.0003011809,0.0006586987,0.0001891387,0.0004615245,0.0002760589,0.0002893169,0.0004098649],"category_scores_gemma":[0.001154459,0.0001205608,0.0001392375,0.001139168,0.0001983237,0.0007148672,0.0003216557,0.000223808,0.00008731196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003201453,"about_ca_system_score_gemma":0.0003057853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004025639,"about_ca_topic_score_gemma":0.01210301,"domain_scores_codex":[0.9998479,0.00004456291,0.00001108892,0.00004860337,0.00003478286,0.00001305791],"domain_scores_gemma":[0.9994229,0.000229535,0.0001365088,0.0000798055,0.0001152145,0.00001602788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002190239,0.0001571081,0.4999506,0.0003169149,0.0001317433,0.0001305358,0.0003461939,0.05154976,0.2172912,0.0007066444,0.0007546692,0.2284455],"study_design_scores_gemma":[0.00001429752,0.0001291402,0.7798122,0.00002846592,0.00007912618,0.0001094495,0.0004376364,0.1844617,0.03042107,0.001861575,0.002591573,0.00005378909],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9409997,0.0004886437,0.05583879,0.0001129135,0.00001193993,0.00003171544,0.0005386724,0.0002265584,0.001751107],"genre_scores_gemma":[0.9883807,0.0001062884,0.01124617,0.00001442711,0.000005928626,0.00001522381,0.0001512628,0.00001036308,0.00006964453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004025639,"threshold_uncertainty_score":0.008004427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646581113199417,"score_gpt":0.2203370471450783,"score_spread":0.2038712360130841,"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."}}