{"id":"W2966976404","doi":"10.1007/s11356-019-06073-2","title":"Optimized extraction of inorganic arsenic species from a foliose lichen biomonitor","year":2019,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Lichen and fungal ecology","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of the Environment, Conservation and Parks","funders":"Department of Higher Education and Training; University of Johannesburg; University of Pretoria","keywords":"Arsenobetaine; Arsenate; Arsenic; Extraction (chemistry); Lichen; Arsenite; Environmental chemistry; Chemistry; Ecotoxicology; Chromatography; Ecology; Biology; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001793523,0.0003672055,0.0002634301,0.0002334546,0.0003025061,0.0002749305,0.0002602232,0.0003974244,0.001539914],"category_scores_gemma":[0.0001897612,0.0001836717,0.00019073,0.0001977342,0.0001441675,0.0002003693,0.0002776185,0.0003437908,0.0007211641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001842604,"about_ca_system_score_gemma":0.0003781118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001855391,"about_ca_topic_score_gemma":0.006132933,"domain_scores_codex":[0.9998264,0.00002832614,0.00001152435,0.00003532399,0.00007357899,0.00002496195],"domain_scores_gemma":[0.9999439,0.00001132412,0.000008649315,0.000004866376,0.00002375009,0.00000757043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003338943,0.000008306849,0.0001191779,0.00003996226,0.00000568982,0.00002067935,0.00001236662,0.00007982264,0.9975616,0.00002002611,0.00003363671,0.002065322],"study_design_scores_gemma":[0.000006624089,0.0001116432,0.001598218,0.000005517747,0.0000121218,0.0001032685,0.00003824601,0.001138121,0.9941761,0.00002381521,0.002776784,0.000009476777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9432416,0.0008094301,0.04998829,0.0001442813,0.00004008665,0.0002823956,0.001015572,0.0005065458,0.003971765],"genre_scores_gemma":[0.9246318,0.001019081,0.06239118,0.0001323941,0.00002562125,0.0001875794,0.001561735,0.00008346566,0.009967169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001855391,"threshold_uncertainty_score":0.00515151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100510748347718,"score_gpt":0.2831323369595295,"score_spread":0.2521272294760523,"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."}}