{"id":"W2150147638","doi":"10.1002/ird.646","title":"AUTOMATIC <i>IN SITU</i> DETERMINATION OF FIELD CAPACITY USING SOIL MOISTURE SENSORS","year":2011,"lang":"en","type":"article","venue":"Irrigation and Drainage","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Field capacity; Water content; Irrigation; Environmental science; Field (mathematics); Drainage; Agricultural engineering; Hydrology (agriculture); Soil science; Environmental engineering; Computer science; Soil water; Engineering; Geotechnical engineering; Mathematics; Agronomy","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":[0.0003118327,0.0004753452,0.0003170702,0.0005927345,0.0001870925,0.0005056546,0.000895546,0.0003705997,0.002086239],"category_scores_gemma":[0.0008755219,0.0002552485,0.0001482572,0.0006720812,0.0002646496,0.0006732582,0.0004375871,0.000397605,0.0007129598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002505291,"about_ca_system_score_gemma":0.0002027001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009444774,"about_ca_topic_score_gemma":0.001727366,"domain_scores_codex":[0.9996957,0.00004486962,0.00001419585,0.0001003206,0.0001171152,0.00002780038],"domain_scores_gemma":[0.999393,0.0001820895,0.0001500329,0.0001186633,0.0001278165,0.0000283854],"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":[0.0001760657,0.0001088115,0.01509093,0.0002352491,0.00002740604,0.00007384608,0.0001746268,0.004038923,0.8677844,0.000594236,0.002260585,0.1094349],"study_design_scores_gemma":[0.00002450052,0.0001696152,0.03160673,0.00002781159,0.00003566841,0.0002930746,0.00012747,0.08972148,0.8708186,0.0009227649,0.006157116,0.0000950881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.476507,0.0003596952,0.5065283,0.0001364791,0.0001429702,0.0001827941,0.00265018,0.006161694,0.007330861],"genre_scores_gemma":[0.901486,0.0001936485,0.09526439,0.00006710073,0.00003255482,0.000140563,0.0005252326,0.0001907474,0.002099804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002086239,"threshold_uncertainty_score":0.006979227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04234183973936735,"score_gpt":0.2341849480485061,"score_spread":0.1918431083091388,"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."}}