{"id":"W3196924810","doi":"10.1016/j.dib.2021.107405","title":"Phosphorus runoff from Canadian agricultural land: A dataset for 30 experimental fields","year":2021,"lang":"en","type":"article","venue":"Data in Brief","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Waterloo; Environment and Climate Change Canada; University of Manitoba; Global Institute for Water Security; University of Saskatchewan","funders":"Agriculture and Agri-Food Canada; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Grain Farmers of Ontario; Ontario Ministry of Agriculture, Food and Rural Affairs; Environment and Climate Change Canada","keywords":"Surface runoff; Snowmelt; Environmental science; Arable land; Hydrology (agriculture); Agriculture; Precipitation; Sediment; Arid; Geography; Ecology; Geology; Meteorology","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":[0.0004300052,0.001087677,0.0006194132,0.003225003,0.001626822,0.0009828077,0.001540453,0.0005823586,0.0033561],"category_scores_gemma":[0.001710414,0.0003666282,0.0007498865,0.01143961,0.0004378193,0.0004045704,0.0005902956,0.0003547223,0.00158595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01991677,"about_ca_system_score_gemma":0.0232609,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9864325,"about_ca_topic_score_gemma":0.9939654,"domain_scores_codex":[0.9991611,0.00003076252,0.00005072578,0.0001591958,0.0004063242,0.0001918492],"domain_scores_gemma":[0.9974204,0.0001346408,0.0001891944,0.0001822463,0.001888628,0.0001848955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008007477,0.0004632403,0.5455428,0.001748318,0.0006494469,0.0006245261,0.0006820713,0.02791219,0.006911226,0.001316488,0.3299771,0.08337185],"study_design_scores_gemma":[0.000134126,0.00004057183,0.859878,0.000108672,0.0001194111,0.00009262194,0.0006351907,0.008289061,0.002415031,0.0002425773,0.1279573,0.00008735013],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09507865,0.000373742,0.0005498765,0.0001286867,0.0000114979,0.0001268538,0.8999679,0.0003705161,0.003392367],"genre_scores_gemma":[0.09263337,0.0004587888,0.001992857,0.00006524659,0.000006741748,0.0002236499,0.902864,0.00004577377,0.001709512],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01991677,"threshold_uncertainty_score":0.144507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768597446490033,"score_gpt":0.2433794579770611,"score_spread":0.2256934835121608,"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."}}