{"id":"W43492304","doi":"10.1023/a:1014798012212","title":"Determination of Total Mercury in Fish Tissues using Combustion Atomic Absorption Spectrometry with Gold Amalgamation","year":2002,"lang":"en","type":"article","venue":"Water Air & Soil Pollution","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; U.S. Fish and Wildlife Service; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; National Oceanic and Atmospheric Administration; University of Nevada, Las Vegas; Office of Research and Development; U.S. Environmental Protection Agency","keywords":"Mercury (programming language); Chemistry; Certified reference materials; Chromatography; Muscle tissue; Detection limit; Skeletal muscle; Inductively coupled plasma mass spectrometry; Mass spectrometry; Anatomy; Biology","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":[0.0004203311,0.0004337404,0.0003142549,0.0005662207,0.0008538032,0.0002915109,0.0004697089,0.0005527078,0.000805801],"category_scores_gemma":[0.0003847301,0.0003381214,0.0004054221,0.0004157012,0.0006400802,0.0002935519,0.0004930285,0.0005021833,0.0004000341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000535201,"about_ca_system_score_gemma":0.001035022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060457,"about_ca_topic_score_gemma":0.02510225,"domain_scores_codex":[0.9996713,0.0000298207,0.00001659325,0.0001068713,0.0001389083,0.00003646273],"domain_scores_gemma":[0.9997944,0.00003252128,0.00002857086,0.00003425154,0.00009131863,0.00001895908],"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":[0.0001209825,0.00001342117,0.003873631,0.00003036287,0.0000225959,0.00002990699,0.00008877507,0.00009696845,0.992521,0.00007520668,0.00004548249,0.003081561],"study_design_scores_gemma":[0.00001123741,0.000197677,0.01437152,0.000004719625,0.00004364617,0.0001661659,0.00004410653,0.0007205599,0.9832667,0.00009965205,0.001063319,0.00001068136],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9527062,0.000516347,0.04144335,0.0001107509,0.00005089085,0.0001090418,0.0004697301,0.0002798146,0.004313917],"genre_scores_gemma":[0.8947809,0.0009174971,0.08462232,0.0002534377,0.0000202149,0.0002094628,0.000813273,0.0001162571,0.01826663],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01060457,"threshold_uncertainty_score":0.02108568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01600704447062506,"score_gpt":0.2327866018844402,"score_spread":0.2167795574138151,"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."}}