{"id":"W4404524549","doi":"10.1016/j.jenvman.2024.123305","title":"Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of the Environment, Conservation and Parks; University of Guelph","funders":"","keywords":"Watershed; Agriculture; Nutrient; Surface water; Environmental science; Water quality; Hydrology (agriculture); Agricultural engineering; Water resource management; Machine learning; Environmental engineering; Computer science; Engineering; Ecology; Biology","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":[0.001162306,0.0008752217,0.0006025101,0.0008957334,0.0003873628,0.0008211213,0.0009052692,0.0007440708,0.0006721904],"category_scores_gemma":[0.003374248,0.000240767,0.0005822359,0.001208609,0.0002856805,0.0004910738,0.0003738336,0.000756507,0.0001851043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002197601,"about_ca_system_score_gemma":0.001955186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2192397,"about_ca_topic_score_gemma":0.140196,"domain_scores_codex":[0.9996703,0.00008799706,0.00003296741,0.00008252979,0.00007934095,0.00004680027],"domain_scores_gemma":[0.9986978,0.0009187583,0.0001386985,0.00002542491,0.000193356,0.00002594908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000511933,0.00005423967,0.009151519,0.00002967671,0.00003616568,0.00003704459,0.00002807538,0.9733589,0.0003910191,0.0002067233,0.0002834502,0.01637201],"study_design_scores_gemma":[0.000002275178,0.000007182543,0.001002518,0.000002217784,0.000003374587,0.000002117456,0.000008856503,0.9987066,0.00009187581,0.0001112893,0.00005979637,0.000001917234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8889794,0.001411034,0.1044788,0.0005421704,0.00004385872,0.0001356452,0.001424281,0.0008542014,0.002130498],"genre_scores_gemma":[0.980741,0.0003336352,0.01684362,0.00003500055,0.00001575025,0.00008747639,0.0009073661,0.00001231159,0.001023812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2192397,"threshold_uncertainty_score":0.4359271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01940810971268389,"score_gpt":0.2141128197977494,"score_spread":0.1947047100850655,"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."}}