{"id":"W1991539439","doi":"10.1111/j.1469-8137.2009.02912.x","title":"Speciation and distribution of arsenic and localization of nutrients in rice grains","year":2009,"lang":"en","type":"article","venue":"New Phytologist","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":259,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; University of Washington; Office of Science; Simon Fraser University; Office of Research and Development; U.S. Environmental Protection Agency; U.S. Department of Energy","keywords":"Genetic algorithm; Endosperm; Micronutrient; Environmental chemistry; Arsenic; Bioavailability; Nutrient; Chemistry; Bran; Inductively coupled plasma mass spectrometry; Biofortification; XANES; Mass spectrometry; Spectroscopy; Chromatography; Biology; Biochemistry; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008182657,0.00004024659,0.00007552921,0.00001953762,0.00001754828,0.000002775575,0.00002466832,0.00003521356,0.00002023062],"category_scores_gemma":[0.0000608153,0.00004018717,0.000008629489,0.0001533561,0.00008453467,0.00009768601,0.00001487229,0.00002508664,0.000001341833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004263967,"about_ca_system_score_gemma":0.000005220194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001554278,"about_ca_topic_score_gemma":0.0001941509,"domain_scores_codex":[0.9995891,0.00002078495,0.0001491094,0.0001006875,0.000085516,0.00005472885],"domain_scores_gemma":[0.999801,0.00001644945,0.0001016108,0.00005097052,0.000006727334,0.00002318342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004521478,0.0001652467,0.8115222,0.00001734867,0.000002568773,0.000001018534,0.0009290934,0.0003045971,0.0146232,0.005986378,0.0008669206,0.1655362],"study_design_scores_gemma":[0.0004502414,0.00006480858,0.9901254,0.00001067632,0.000004032372,0.000001269301,0.00006039022,0.002009522,0.003915256,0.002737651,0.0005816982,0.00003901815],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847686,0.00004197292,0.01342386,0.0002309868,0.00001890855,0.0001072239,0.000004744632,0.000005831816,0.001397828],"genre_scores_gemma":[0.9996564,0.00007255923,0.0001281034,0.00003792179,0.000006000445,6.559212e-7,0.00002915046,0.00000105862,0.00006813516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1786032,"threshold_uncertainty_score":0.1638785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009209122427987928,"score_gpt":0.2328689019126662,"score_spread":0.2236597794846783,"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."}}