{"id":"W2375203574","doi":"","title":"Nitrogen pollution and spatial distribution pattern of Wuliangsuhai Lake","year":2006,"lang":"en","type":"article","venue":"Geographical Research","topic":"Environmental Quality and Pollution","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Eutrophication; Sediment; Pollution; Nutrient pollution; Environmental science; Nitrogen; Surface water; Hydrology (agriculture); Pollutant; Water quality; Spatial distribution; Environmental chemistry; Ecology; Nutrient; Environmental engineering; Geography; Geology; Biology; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001233331,0.0001938994,0.000197116,0.001000152,0.0003453511,0.0003026683,0.0001689578,0.0002051477,0.0003667671],"category_scores_gemma":[0.0002337354,0.0001566703,0.0002171265,0.001072107,0.0002604848,0.0002376536,0.0003192928,0.00009213849,0.00004036789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005939736,"about_ca_system_score_gemma":0.0005416212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06170931,"about_ca_topic_score_gemma":0.1055629,"domain_scores_codex":[0.9998381,0.00001201857,0.0000128826,0.00005958803,0.00004165974,0.00003565542],"domain_scores_gemma":[0.999778,0.00001570143,0.00007986628,0.000009656907,0.00007779938,0.00003896691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004566984,0.00001156086,0.9896226,0.00002403038,0.00005334429,0.0001896555,0.0005050357,0.0003282545,0.005330524,0.00006968543,0.000146617,0.003673022],"study_design_scores_gemma":[0.000001121775,0.000008260919,0.9991698,0.000001664894,0.000008839477,0.00003235974,0.0001283823,0.0003621155,0.0001327367,0.0000128037,0.0001387079,0.000003121564],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999545,0.00004939925,0.00005260906,0.00001284246,9.125928e-7,0.000002846485,0.0001164571,0.000004912449,0.0002150077],"genre_scores_gemma":[0.9994066,0.00003850601,0.00008490236,0.000006657891,0.000002474972,0.000005836509,0.0002553534,9.172433e-7,0.0001987333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06170931,"threshold_uncertainty_score":0.1227003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949186214581547,"score_gpt":0.2892824411862012,"score_spread":0.2697905790403857,"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."}}