{"id":"W2805178566","doi":"10.1139/cjss-2017-0144","title":"Dry matter partitioning and residue N content for 11 major field crops in Canada adjusted for rooting depth and yield","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Residue (chemistry); Agronomy; Dry matter; Yield (engineering); Shoot; Grain yield; Environmental science; Field experiment; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004009579,0.00006086586,0.00010611,0.00003462999,0.0003057005,0.00009137994,0.0001414754,0.0000294547,0.00002350527],"category_scores_gemma":[0.0003332369,0.00002991077,0.00002065492,0.0001754877,0.0001595819,0.0001335777,0.00001220708,0.00006027273,1.581281e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001195328,"about_ca_system_score_gemma":0.0005594529,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9929999,"about_ca_topic_score_gemma":0.9999878,"domain_scores_codex":[0.9992758,0.000009771641,0.000183323,0.0001284949,0.00009834307,0.0003042852],"domain_scores_gemma":[0.9991532,0.0002107691,0.0000897444,0.00002236788,0.0001866317,0.000337295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001045787,0.000001075069,0.9946566,0.000003939745,0.00000141581,0.000004734974,0.00006061367,0.000002357031,0.0007488288,0.00005687525,0.0002267358,0.004226346],"study_design_scores_gemma":[0.0001405707,0.0001489245,0.9942393,0.00005830035,0.000005257625,0.00002233252,0.0008521908,0.001428572,0.00268569,0.000162158,0.0001836073,0.00007305563],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970539,0.0001157345,0.00001562872,0.002416582,0.0001738972,0.00009446972,0.00001593013,0.000001123089,0.000112793],"genre_scores_gemma":[0.9984512,0.000002891215,0.0002203911,0.001167906,0.0001187677,0.000003524598,7.046913e-7,6.85903e-7,0.00003391028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006987878,"threshold_uncertainty_score":0.2351232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03281189263277556,"score_gpt":0.2132680189461903,"score_spread":0.1804561263134147,"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."}}