{"id":"W16047627","doi":"","title":"Land use interactions drive southwestern Ontario stream nutrient concentrations","year":2014,"lang":"en","type":"article","venue":"Hospitals & health networks","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agriculture and Agri-Food Canada; U.S. Geological Survey","keywords":"Nutrient; Environmental science; Land use; Hydrology (agriculture); Geology; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001290061,0.0001533105,0.0001544117,0.0003357705,0.0006485583,0.0008536556,0.0002841639,0.0001848611,0.001848276],"category_scores_gemma":[0.0006799184,0.0001417199,0.0002400839,0.0009482485,0.0003569082,0.0001929541,0.0003837537,0.0001482633,0.0001843759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005698937,"about_ca_system_score_gemma":0.004940343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.923331,"about_ca_topic_score_gemma":0.9760918,"domain_scores_codex":[0.9998858,0.00001359964,0.000006657279,0.00003462816,0.00003276765,0.00002648372],"domain_scores_gemma":[0.9996088,0.00007339074,0.0001009665,0.00001841119,0.0001286216,0.00006987263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005484174,0.00001332827,0.9904662,0.00002815246,0.00004962928,0.0001317654,0.0008140269,0.000692473,0.001465491,0.0001773852,0.001072602,0.005034095],"study_design_scores_gemma":[0.000002723199,0.000005975662,0.9970368,0.000007002318,0.00001272561,0.00001759179,0.0006032561,0.0009926358,0.00008417552,0.00004705824,0.001186265,0.000003713973],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967057,0.0001171779,0.00008985303,0.0001389882,0.000003697983,0.000007285515,0.0008907726,0.000009937001,0.002036647],"genre_scores_gemma":[0.9974232,0.0001632765,0.0001479081,0.00002678308,0.000002578269,0.000007578848,0.000450251,0.0000047517,0.001773654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07666898,"threshold_uncertainty_score":0.154241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009701818003390615,"score_gpt":0.230507314940718,"score_spread":0.2208054969373274,"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."}}