{"id":"W1960909337","doi":"10.1139/s08-041","title":"Modelling nitrogen composition in streams on the Boreal Plain using genetic adaptive general regression neural networks","year":2008,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Alberta Health Services; Canadian Natural Resources; University of Alberta","funders":"","keywords":"Watershed; STREAMS; Environmental science; Artificial neural network; Hydrology (agriculture); Regression; Water quality; Mean squared error; Computer science; Ecology; Machine learning; Statistics; Mathematics; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003210409,0.0003234337,0.0001845143,0.0003123653,0.0002491425,0.0003736054,0.0002996701,0.0002793625,0.0001638509],"category_scores_gemma":[0.0006730632,0.0001300084,0.000233077,0.0003501429,0.0002200243,0.0002903405,0.0001233954,0.0001651183,0.00002677648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000931423,"about_ca_system_score_gemma":0.0008633145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1693598,"about_ca_topic_score_gemma":0.178424,"domain_scores_codex":[0.9999297,0.00001860975,0.000004997154,0.00001953368,0.00001545353,0.00001165273],"domain_scores_gemma":[0.9998296,0.00008003496,0.00003332601,0.000006258202,0.00004144737,0.000009224851],"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":[0.00004541892,0.00003626995,0.02323026,0.00001059811,0.00002042548,0.00004164402,0.00002761244,0.9653245,0.001889264,0.0001613304,0.00005773644,0.00915493],"study_design_scores_gemma":[0.000004296963,0.00001466935,0.006080822,0.000001098522,0.000005375872,0.000004317872,0.00001425896,0.9932566,0.0004402724,0.0001335048,0.00004159982,0.000003165591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866927,0.00005173151,0.01250996,0.00003736792,0.00000482461,0.00001379943,0.00006421859,0.00007523912,0.0005502647],"genre_scores_gemma":[0.9946755,0.00004000039,0.004930263,0.000004431071,0.000002430589,0.000008241033,0.00007456655,0.000002905793,0.0002617441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1693598,"threshold_uncertainty_score":0.3367479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063669630692865,"score_gpt":0.1996206030866489,"score_spread":0.1789839067797202,"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."}}