{"id":"W2065421895","doi":"10.1061/(asce)0733-9372(2009)135:4(218)","title":"Cost-Effective Approach for Continuous Major Ion and Nutrient Concentration Estimation in a River","year":2009,"lang":"en","type":"article","venue":"Journal of Environmental Engineering","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Biogeochemical cycle; Nutrient; Environmental science; Nitrate; Context (archaeology); Hydrology (agriculture); Ammonium; Water quality; Environmental chemistry; Total dissolved solids; Chemistry; Environmental engineering; Ecology; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001399532,0.0007656785,0.0008044262,0.001295903,0.0003311233,0.001066158,0.001498893,0.0007830984,0.001158955],"category_scores_gemma":[0.002863509,0.0005436528,0.0003845589,0.001093406,0.0002857301,0.00088752,0.0007837214,0.0005653298,0.000620464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006498273,"about_ca_system_score_gemma":0.0007943416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003254565,"about_ca_topic_score_gemma":0.005924654,"domain_scores_codex":[0.9984558,0.0002843248,0.00006630766,0.0002458069,0.000905316,0.00004248862],"domain_scores_gemma":[0.9984761,0.0005376066,0.0001477122,0.0001869676,0.0006075224,0.00004411281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000704272,0.0005463927,0.02291286,0.0004278042,0.0001368391,0.0001797039,0.000134753,0.02049032,0.440453,0.001091518,0.001253482,0.5116691],"study_design_scores_gemma":[0.0001762351,0.001375178,0.03179071,0.00003281153,0.0003619273,0.000831013,0.0001918369,0.5736936,0.3831544,0.001442538,0.006742976,0.0002067903],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2313229,0.0006289579,0.763992,0.0002257487,0.0000725397,0.0002782548,0.0003764801,0.001405711,0.001697388],"genre_scores_gemma":[0.4094627,0.0003067141,0.5879221,0.0000594194,0.00002981678,0.0003197066,0.0003210389,0.00006415737,0.001514323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003254565,"threshold_uncertainty_score":0.007401526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009214236039319212,"score_gpt":0.2305869915142987,"score_spread":0.2213727554749795,"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."}}