{"id":"W4406016738","doi":"10.1038/s41467-024-55714-9","title":"Restoring small water bodies to improve lake and river water quality in China","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Water quality; China; Environmental science; Agriculture; Nutrient; Population; Water resource management; Geography; Ecology; Environmental health; Biology","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":[0.0005008414,0.0003039559,0.0003100566,0.0006503965,0.000604535,0.0005526609,0.0005861584,0.0002650614,0.001089085],"category_scores_gemma":[0.0005132153,0.000108275,0.0004214561,0.0009982684,0.0007380617,0.0006258365,0.0008359632,0.0001623499,0.00006302614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0018339,"about_ca_system_score_gemma":0.003470529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05986284,"about_ca_topic_score_gemma":0.1383335,"domain_scores_codex":[0.9998152,0.00002591492,0.00001546977,0.00003728845,0.00005084673,0.00005521566],"domain_scores_gemma":[0.9997757,0.00001542265,0.00006128331,0.00002446625,0.00005428608,0.00006889934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003620528,0.0005580457,0.6026291,0.0008524458,0.0003826298,0.0009162283,0.001552303,0.03280335,0.09864516,0.002338861,0.005733553,0.2532264],"study_design_scores_gemma":[0.00005282345,0.0002792594,0.9609565,0.00004051733,0.0001418479,0.00008467188,0.001005539,0.01697734,0.00991448,0.001024844,0.009489849,0.00003240743],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966312,0.0003294513,0.001357276,0.0003495616,0.00001172901,0.00003349898,0.0001712568,0.00008981453,0.001026106],"genre_scores_gemma":[0.9986082,0.0001627681,0.0006249978,0.00006711137,0.000003446418,0.00001338493,0.0001254967,0.000005465403,0.0003892585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05986284,"threshold_uncertainty_score":0.1190288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01533531871758154,"score_gpt":0.2858658787647193,"score_spread":0.2705305600471378,"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."}}