{"id":"W4247743827","doi":"10.32920/ryerson.14667981","title":"Then and Now: Understanding Change on an Agricultural Landscape","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Statistics Canada","funders":"","keywords":"Hectare; Agriculture; Agricultural land; Eutrophication; Nonpoint source pollution; Environmental science; Water quality; Land use; Phosphorus; Land management; Pollution; Hydrology (agriculture); Watershed; Water resource management; Environmental protection; Geography; Nutrient; Ecology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003887346,0.0002515213,0.0002199316,0.0008645711,0.001005727,0.004390004,0.0005422374,0.0005973968,0.003581847],"category_scores_gemma":[0.0009177973,0.00008962001,0.0002042362,0.0015546,0.003110132,0.00619206,0.00111214,0.0007631664,0.0002689284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002757474,"about_ca_system_score_gemma":0.001436164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06000596,"about_ca_topic_score_gemma":0.09605396,"domain_scores_codex":[0.9998337,0.00004668466,0.000005074112,0.00005268993,0.00002831959,0.00003338665],"domain_scores_gemma":[0.9997612,0.00005771992,0.00004919633,0.00003186202,0.00004891245,0.00005117404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005999581,0.0001513957,0.2832091,0.0007111116,0.0001224795,0.0008099751,0.04989634,0.005550327,0.005032196,0.2776917,0.02604507,0.3507203],"study_design_scores_gemma":[0.000004378335,0.00005200936,0.347708,0.0003663668,0.000034252,0.000273685,0.08564567,0.003878679,0.0005971058,0.3148741,0.246535,0.00003082158],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7109718,0.01963299,0.02086805,0.05319628,0.000394606,0.00004737276,0.001713018,0.0001445951,0.1930314],"genre_scores_gemma":[0.9740897,0.00953095,0.006868354,0.001614457,0.000142994,0.00002517243,0.0004571964,0.00004222277,0.007228936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06000596,"threshold_uncertainty_score":0.1193134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05508129904492331,"score_gpt":0.2356191780115635,"score_spread":0.1805378789666402,"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."}}