{"id":"W2084075863","doi":"10.1007/s10311-006-0054-1","title":"A comparison of SEM-EDS with ICP-AES for the quantitative elemental determination of estuarine particles","year":2006,"lang":"en","type":"article","venue":"Environmental Chemistry Letters","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; Leverhulme Trust","keywords":"Estuary; Scanning electron microscope; Particulates; Inductively coupled plasma atomic emission spectroscopy; Sediment; Inductively coupled plasma; Particle (ecology); Elemental analysis; Environmental chemistry; Analytical Chemistry (journal); Inductively coupled plasma mass spectrometry; Particle size; Mass spectrometry; Chemistry; Materials science; Mineralogy; Geology; Physics; Chromatography; Oceanography; Plasma; Inorganic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246239,0.0003640115,0.0004393977,0.0009992688,0.0003467691,0.0005640327,0.0007680342,0.000646556,0.002759141],"category_scores_gemma":[0.001287101,0.0003885201,0.0003627232,0.00072897,0.0002101202,0.0006435819,0.0004539953,0.0003301842,0.000967285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123226,"about_ca_system_score_gemma":0.0002494855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001582707,"about_ca_topic_score_gemma":0.007805287,"domain_scores_codex":[0.9992259,0.0001157075,0.00005848911,0.00009399657,0.0004759326,0.00002992119],"domain_scores_gemma":[0.998976,0.0003920038,0.00004552252,0.0001045154,0.0004521677,0.00002984916],"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":[0.001053383,0.00008531682,0.007318641,0.0004806723,0.00009942682,0.0001209599,0.0001279188,0.0006694898,0.9336583,0.0002815325,0.0007681963,0.05533616],"study_design_scores_gemma":[0.00002651945,0.0003297062,0.02594426,0.00001948678,0.00009862834,0.0008577429,0.00009770656,0.005480871,0.9595723,0.0001782413,0.007369005,0.00002550379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7357063,0.009807559,0.2317474,0.0004890982,0.0003380906,0.0001805676,0.002855638,0.001406016,0.01746936],"genre_scores_gemma":[0.8018944,0.00368626,0.170222,0.0002321363,0.00004908439,0.00007335668,0.001375123,0.0003838293,0.02208384],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002759141,"threshold_uncertainty_score":0.009230196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274931829383171,"score_gpt":0.2604906004269793,"score_spread":0.2477412821331476,"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."}}