{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009467293,0.00008147308,0.00009916504,0.00005338724,0.0001939756,0.00001890914,0.0001180977,0.0001133736,0.001000055],"category_scores_gemma":[0.00005204459,0.00007578797,0.00005443456,0.0003905514,0.001016346,0.00009498192,0.0002171509,0.0002364154,0.0001181329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004978386,"about_ca_system_score_gemma":0.000004170482,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01521845,"about_ca_topic_score_gemma":0.007050827,"domain_scores_codex":[0.9982412,0.0002432814,0.0002007429,0.0002549071,0.0007078419,0.0003519918],"domain_scores_gemma":[0.9995978,0.00009211405,0.00003777077,0.0001645565,0.000008343664,0.00009943466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004861887,0.0002100504,0.9432949,0.00001692502,0.000006180891,0.000002800627,0.00003856626,0.00004947845,0.0108409,0.0008502862,0.001696133,0.04294514],"study_design_scores_gemma":[0.0002555342,0.000120162,0.9757499,0.00000940983,0.00000528577,0.000003192711,0.00002907887,0.0004501689,0.002324281,0.01058411,0.01038092,0.00008799484],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935146,0.00006237979,0.003655386,0.001166699,0.00002213289,0.0001813483,0.000182318,0.00001543279,0.001199706],"genre_scores_gemma":[0.9995663,0.00005393318,0.00004423597,0.00004981688,0.00005036037,0.0000125223,0.0001508362,0.000005642511,0.0000663107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04285715,"threshold_uncertainty_score":0.9999132,"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."}}