{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002178861,0.000547352,0.0005100547,0.000203586,0.0003752219,0.0001154541,0.000378748,0.0004012451,0.0008634602],"category_scores_gemma":[0.00001540155,0.0003934674,0.0001366619,0.0001528533,0.00006076918,0.000438185,0.0001589228,0.0005730292,0.0004543708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001437255,"about_ca_system_score_gemma":0.00000382321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002381058,"about_ca_topic_score_gemma":0.00005365858,"domain_scores_codex":[0.9975616,0.00004673352,0.0006850499,0.0005927894,0.0003864447,0.0007274016],"domain_scores_gemma":[0.9988594,0.0000429043,0.00006842862,0.0006328298,0.0001703239,0.0002261179],"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":[0.0001318192,0.0004487963,0.00003602358,0.004624368,0.0009168023,0.00003563322,0.00005969326,0.004369171,0.7445477,0.1890657,0.0001075034,0.0556568],"study_design_scores_gemma":[0.001050125,0.0001586562,0.0003149047,0.0003251203,0.0005695955,0.0000158473,0.0005243606,0.001977898,0.8510703,0.0243798,0.1178766,0.001736825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9388044,0.0001960603,0.000004328124,0.000009324021,0.0003031333,0.0008583413,0.0002241251,0.0002223969,0.0593779],"genre_scores_gemma":[0.9803631,0.0003289985,0.0005308109,0.00007448762,0.00002030753,0.0002995145,0.0008261389,0.0001750154,0.01738163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1646859,"threshold_uncertainty_score":0.9998517,"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."}}