{"id":"W4372203598","doi":"10.1016/j.watres.2023.120054","title":"Size and temperature drive nutrient retention potential across water bodies in China","year":2023,"lang":"en","type":"article","venue":"Water Research","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Nutrient; Eutrophication; Environmental science; Wetland; Biogeochemical cycle; Lake ecosystem; Surface runoff; Hydrology (agriculture); Nutrient cycle; Land cover; Ecology; Ecosystem; Land use; 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":[0.0003232223,0.00018324,0.000320232,0.001055715,0.0006121111,0.0006797852,0.0005419762,0.000277864,0.001132762],"category_scores_gemma":[0.0005309312,0.0002657109,0.0004818607,0.001678564,0.0007312967,0.0006862306,0.0006445036,0.000184102,0.0001146173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001519267,"about_ca_system_score_gemma":0.001222752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1230418,"about_ca_topic_score_gemma":0.2117472,"domain_scores_codex":[0.9997742,0.00002780565,0.00001644394,0.00007431504,0.00003540066,0.00007187654],"domain_scores_gemma":[0.9994299,0.0001239635,0.0001428251,0.0000435044,0.0001128751,0.0001469127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004979778,0.00002673495,0.993397,0.00001096735,0.00007852272,0.00007254122,0.00061482,0.0007289264,0.002020366,0.000238664,0.0001419929,0.002619603],"study_design_scores_gemma":[0.000001840833,0.000006402518,0.9984317,9.756404e-7,0.00001029243,0.00000816904,0.0002881886,0.001043377,0.00005633021,0.00006687049,0.00008138677,0.000004406063],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994951,0.00002161339,0.00006914148,0.00002925719,0.000001220292,0.0000012816,0.00007217281,0.000003335,0.0003068522],"genre_scores_gemma":[0.9997254,0.00001037874,0.00002241169,0.000006446194,0.000001156504,0.000001529094,0.00006391087,0.000001367222,0.0001674515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1230418,"threshold_uncertainty_score":0.2446513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823597703037291,"score_gpt":0.2947328897907203,"score_spread":0.2764969127603474,"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."}}