{"id":"W4412035281","doi":"10.46770/as.2025.067","title":"A Miniature Purge-and-Trap Using a Gold-plated Wire for Field Detection of Ultra-Trace Mercury in Water by Microplasma Optical Emission Spectrometry","year":2025,"lang":"en","type":"article","venue":"Atomic Spectroscopy","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Fundamental Research Funds for the Central Universities; Sichuan University; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Mercury (programming language); Microplasma; Chemistry; Mass spectrometry; Trap (plumbing); Analytical Chemistry (journal); Purge; Environmental chemistry; Chromatography; Plasma; Waste management; Environmental engineering","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":[0.0003481748,0.0009057762,0.0005626105,0.0004842671,0.0002586483,0.0004024112,0.001596789,0.0009889223,0.0008417644],"category_scores_gemma":[0.0004277808,0.000626932,0.0003644898,0.0002603038,0.0004833671,0.0009430934,0.0007686437,0.0005980039,0.0005307698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004116479,"about_ca_system_score_gemma":0.0002718006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000473557,"about_ca_topic_score_gemma":0.0008409847,"domain_scores_codex":[0.9994658,0.00004773978,0.00002404718,0.0001935749,0.0002329765,0.00003590586],"domain_scores_gemma":[0.9997922,0.00006100018,0.00004884591,0.00003448197,0.00004519094,0.00001839006],"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":[0.00003560194,0.00001808477,0.0002293104,0.00009118598,0.00001132048,0.00008289477,0.0000279655,0.0001145474,0.9906788,0.0002026118,0.0002207245,0.008286991],"study_design_scores_gemma":[0.00001547321,0.0002045017,0.001141258,0.000004829183,0.00002440604,0.0006433678,0.00002077403,0.005868014,0.9884779,0.00007685069,0.003486152,0.00003657929],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5038568,0.003936861,0.482935,0.0006699727,0.0003815104,0.0007088824,0.0007098498,0.003953397,0.002847743],"genre_scores_gemma":[0.5784867,0.001624461,0.4113809,0.0003116129,0.0001187797,0.0005343102,0.0004301482,0.000140408,0.006972765],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001596789,"threshold_uncertainty_score":0.002986729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00866577736361325,"score_gpt":0.2856939123482417,"score_spread":0.2770281349846285,"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."}}