{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000181199,0.0001965484,0.0002231986,0.00002706808,0.0003480989,0.0001137711,0.0001708522,0.00007613961,0.0002186747],"category_scores_gemma":[0.0000140965,0.0001786925,0.0000741668,0.000164251,0.0001146942,0.0003726653,0.0001161591,0.0003027901,0.0002409264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005596312,"about_ca_system_score_gemma":0.0000381525,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04536584,"about_ca_topic_score_gemma":0.08688921,"domain_scores_codex":[0.9983423,0.00009521931,0.0003805455,0.0003914643,0.0002229232,0.0005675011],"domain_scores_gemma":[0.9989868,0.0001165302,0.0001861707,0.0003525271,0.00001539881,0.0003425415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001112214,0.0001129743,0.9821989,0.000003158087,0.000008938537,0.000001472297,0.001421895,0.008929444,3.806033e-7,0.0001776389,0.003286114,0.003847973],"study_design_scores_gemma":[0.0005433753,0.0002594042,0.9255528,0.00005374703,0.00001470115,0.000005411399,0.00006705753,0.02312213,0.000003677566,0.0009735331,0.04914996,0.0002541419],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670743,0.00002856137,0.02955105,0.0007580761,0.0008939386,0.0004162692,0.00001488254,0.0000954514,0.00116744],"genre_scores_gemma":[0.9964622,0.00007532325,0.0006318809,0.001402402,0.000164508,0.00004209197,0.0001400379,0.00002089524,0.001060709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05664603,"threshold_uncertainty_score":0.9609911,"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."}}