{"id":"W4285236376","doi":"10.1039/d2em00118g","title":"Atom probe tomography and transmission electron microscopy: a powerful combination to characterize the speciation and distribution of Cu in organic matter","year":2022,"lang":"en","type":"article","venue":"Environmental Science Processes & Impacts","topic":"Advanced Materials Characterization Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Environmental Molecular Sciences Laboratory","keywords":"Organic matter; Nanoparticle; Magnetite; Chemistry; Transmission electron microscopy; Amorphous solid; Atom probe; Silanol; Mineralization (soil science); Mineralogy; Nucleation; Soil water; Chemical engineering; Environmental chemistry; Materials science; Nanotechnology; Geology; Metallurgy; Crystallography; Organic 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":[0.0002945513,0.0004619012,0.0002484818,0.0009379498,0.0001957445,0.0004242795,0.0004502795,0.0006608526,0.002071616],"category_scores_gemma":[0.0005098092,0.0003057673,0.0001952812,0.0006773166,0.0003770674,0.0008380034,0.0005752474,0.000522674,0.0004897023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002363825,"about_ca_system_score_gemma":0.0002756155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006741722,"about_ca_topic_score_gemma":0.001424656,"domain_scores_codex":[0.9998351,0.00002945683,0.000008736627,0.00004236585,0.00006610611,0.00001815588],"domain_scores_gemma":[0.9995883,0.0001662172,0.00007545935,0.00007189978,0.00006935463,0.0000288078],"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":[0.00009684275,0.00003399183,0.002562554,0.0002120675,0.00003042617,0.0002601425,0.00005796758,0.0009314409,0.9694405,0.00174653,0.0004091225,0.02421845],"study_design_scores_gemma":[0.0000306557,0.0003416685,0.02003532,0.00005178429,0.00008388732,0.003922847,0.0002734239,0.07261467,0.8862263,0.002816406,0.0135368,0.00006628493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3725396,0.005005335,0.6022757,0.0006684137,0.0002449703,0.0002526454,0.001602728,0.002282209,0.01512847],"genre_scores_gemma":[0.6601983,0.002611241,0.3319465,0.0002767931,0.00006913883,0.0001950397,0.0005349261,0.0001186536,0.004049479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002071616,"threshold_uncertainty_score":0.006930232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002688845705640625,"score_gpt":0.204825396389832,"score_spread":0.2021365506841914,"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."}}