{"id":"W2970156891","doi":"10.5194/bg-17-2701-2020","title":"Quantifying the contributions of riverine vs. oceanic nitrogen to hypoxia in the East China Sea","year":2020,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Compute Canada","keywords":"Hypoxia (environmental); Nutrient; Biogeochemical cycle; Environmental science; China sea; Oceanography; Nitrogen; Estuary; Oxygen; Ecology; Geology; Biology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002271925,0.0005033572,0.0002028034,0.0003192203,0.0002600957,0.0004680313,0.0003756676,0.0004159602,0.0004186188],"category_scores_gemma":[0.0003325434,0.000226718,0.0005491789,0.0003467093,0.0002476691,0.0005732892,0.0004527218,0.0002135141,0.00004130525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009925081,"about_ca_system_score_gemma":0.0008427736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06013914,"about_ca_topic_score_gemma":0.04936498,"domain_scores_codex":[0.9999444,0.000009292153,0.000004243528,0.00001986906,0.000009386518,0.00001268603],"domain_scores_gemma":[0.9999007,0.00002666408,0.00002199878,0.000008558668,0.00001897538,0.000023062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002177856,0.0001479468,0.3796415,0.00007348957,0.0002240458,0.0002124219,0.0001165629,0.5923318,0.01806202,0.0006234223,0.0002424925,0.00810654],"study_design_scores_gemma":[0.00002721155,0.00006982425,0.128052,0.000007537604,0.00006344826,0.0000207655,0.00009402289,0.868176,0.002849474,0.000328835,0.0002912767,0.00001950338],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982632,0.00004278018,0.001165495,0.00003098538,0.00000486738,0.000004421895,0.0001369259,0.00002190261,0.0003294389],"genre_scores_gemma":[0.999088,0.00003701754,0.000574589,0.000006105679,0.000001464839,0.000004293277,0.0001448049,0.000004626379,0.0001390891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06013914,"threshold_uncertainty_score":0.1195782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02684432848177539,"score_gpt":0.2289593536795324,"score_spread":0.202115025197757,"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."}}