{"id":"W2792164768","doi":"10.1002/2017gb005824","title":"Present and Future Mercury Concentrations in Chinese Rice: Insights From Modeling","year":2018,"lang":"en","type":"article","venue":"Global Biogeochemical Cycles","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mercury (programming language); Methylmercury; Environmental chemistry; Biogeochemical cycle; Environmental science; Paddy field; Rice plant; Chemistry; Agronomy; Bioaccumulation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002948978,0.000130529,0.0001323079,0.00001019989,0.0001054977,0.00002678868,0.0001010726,0.00008050896,0.0002602239],"category_scores_gemma":[0.00002447053,0.0001005533,0.00002745366,0.0002315844,0.0002689524,0.0001618283,0.0001578675,0.00006581793,0.0001016187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007252537,"about_ca_system_score_gemma":0.000006393447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001497819,"about_ca_topic_score_gemma":0.0003720523,"domain_scores_codex":[0.9991668,0.00002521329,0.0001734787,0.0002627338,0.0001795587,0.0001922338],"domain_scores_gemma":[0.9996867,0.00002420039,0.00003067188,0.0001243285,0.00001078349,0.0001233364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005624048,0.0001258265,0.8392471,0.000005926997,0.00003176704,0.000006243414,0.001740071,0.00004807146,0.1480808,0.0004669052,0.005247836,0.004943214],"study_design_scores_gemma":[0.001639964,0.00009648454,0.8344801,0.00004716276,0.00005520009,0.00001216417,0.001938464,0.02259832,0.02400511,0.09454881,0.01970766,0.000870574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911704,0.0009297059,0.00004835336,0.001225526,0.0001065135,0.0001037747,0.0001238239,0.00002966317,0.006262246],"genre_scores_gemma":[0.9986188,0.0001652553,0.0004784798,0.000237832,0.0004360385,0.000006826466,0.00005059389,0.000003110278,0.000003029578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1240757,"threshold_uncertainty_score":0.4100443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148905288334764,"score_gpt":0.2658457383213984,"score_spread":0.2543566854380508,"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."}}