{"id":"W7053634684","doi":"","title":"Water and nitrogen use efficiency of corn (Zea mays L.) under water table management","year":2013,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Magneto-Optical Properties and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Water balance; Drainage; Leaching (pedology); Water-use efficiency; Water table; Nitrogen; Water use; Nitrogen balance; Irrigation; Fertilizer","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000131186,0.0001552388,0.0001769552,0.0002391561,0.0002466854,0.0003097875,0.0001620075,0.0001332901,0.0002651421],"category_scores_gemma":[0.0001440225,0.0001050068,0.0001727457,0.0002240694,0.0002136568,0.0002588809,0.0001274715,0.0001752867,0.0000596354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003026243,"about_ca_system_score_gemma":0.0006342246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2157938,"about_ca_topic_score_gemma":0.3791531,"domain_scores_codex":[0.9998866,0.00001072875,0.000006110618,0.00003301706,0.00003172441,0.00003177158],"domain_scores_gemma":[0.999858,0.00001666248,0.00004545313,0.000008209274,0.00003595998,0.00003568218],"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":[0.001075716,0.0004087989,0.2241589,0.00004719705,0.0001002842,0.000219872,0.0002634052,0.001903348,0.7633431,0.00009560477,0.0002257769,0.008158107],"study_design_scores_gemma":[0.0000141069,0.0003352718,0.9771097,0.000001522524,0.00001958447,0.00003928829,0.0001360325,0.002025296,0.01993958,0.00002883519,0.0003370791,0.00001368954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998153,0.00001893605,0.00002324645,0.00000325346,3.788719e-7,0.000001474534,0.00005252831,0.000001432804,0.00008337555],"genre_scores_gemma":[0.99922,0.00003941713,0.0000908664,0.00001483141,5.853223e-7,0.000004160451,0.0002483783,0.000002489261,0.000379226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2157938,"threshold_uncertainty_score":0.4290755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273698763716981,"score_gpt":0.1956799734330362,"score_spread":0.1829429857958664,"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."}}