{"id":"W2056729641","doi":"10.1007/s10967-012-2056-8","title":"Reversed-phase extraction chromatography–neutron activation analysis (RPEC–NAA) for copper in natural waters using Amberlite XAD-4 resin coated with 1-(2-thiazolylazo)-2-naphthol","year":2012,"lang":"en","type":"article","venue":"Journal of Radioanalytical and Nuclear Chemistry","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Atomic Energy (Canada); Dalhousie University","funders":"","keywords":"Amberlite; Extraction (chemistry); Chemistry; Copper; Chelating resin; Neutron activation analysis; Chromatography; Detection limit; Solid phase extraction; High-performance liquid chromatography; Matrix (chemical analysis); Neutron activation; Nuclear chemistry; Radiochemistry; Neutron; Adsorption","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":[0.0009271857,0.001540385,0.0009978984,0.001479904,0.001794303,0.0005757679,0.001437037,0.0006904753,0.004453269],"category_scores_gemma":[0.001656544,0.0007149074,0.0006735598,0.0007469339,0.00101188,0.0006696874,0.0004880718,0.001432695,0.002372118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122595,"about_ca_system_score_gemma":0.002405219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007392761,"about_ca_topic_score_gemma":0.01545959,"domain_scores_codex":[0.9991009,0.000151846,0.00008685687,0.0002963913,0.0002421085,0.0001219028],"domain_scores_gemma":[0.9990977,0.0002220695,0.00008035005,0.0001525855,0.0003883465,0.00005892141],"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":[0.0002554419,0.0000591402,0.0005526712,0.0002121509,0.00004621366,0.0001746227,0.0001063581,0.0001728829,0.9873793,0.0003230777,0.0004213265,0.01029685],"study_design_scores_gemma":[0.00004646477,0.0002828,0.00336952,0.00004285453,0.00007675912,0.0005661048,0.00007788675,0.00218312,0.9839126,0.0004029169,0.008994495,0.00004447708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6019124,0.00580819,0.3591819,0.0008204018,0.0005109326,0.001895279,0.002838222,0.008449646,0.01858299],"genre_scores_gemma":[0.5702537,0.008026525,0.3736955,0.0008015773,0.0002512569,0.001949982,0.007067122,0.001316164,0.03663809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007392761,"threshold_uncertainty_score":0.0148977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054870731812201,"score_gpt":0.2827057619117453,"score_spread":0.2721570545936233,"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."}}