{"id":"W2070978855","doi":"10.1021/ac504545w","title":"Development of a Particle-Trap Preconcentration-Soft Ionization Mass Spectrometric Technique for the Quantification of Mercury Halides in Air","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; McGill University","funders":"Environment Canada; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Chemistry; Mercury (programming language); Halide; Ionization; Trap (plumbing); Ion trap; Environmental chemistry; Mass spectrometry; Analytical Chemistry (journal); Chromatography; Inorganic chemistry; Ion; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006041091,0.000586464,0.0004080666,0.0007574485,0.0005647708,0.0004556183,0.0007080056,0.0006982543,0.001121921],"category_scores_gemma":[0.000846552,0.0003549749,0.0004311142,0.0004258109,0.0005138524,0.0004380963,0.0005498354,0.0007575206,0.0008765595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000675678,"about_ca_system_score_gemma":0.001073108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002241643,"about_ca_topic_score_gemma":0.007059837,"domain_scores_codex":[0.9992348,0.00008470185,0.0000306628,0.0001780126,0.0004392793,0.0000326049],"domain_scores_gemma":[0.9995944,0.0001172035,0.00006380391,0.00003518758,0.0001449986,0.00004450002],"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":[0.00002598083,0.00002143588,0.0007631624,0.00008543228,0.00001744547,0.00003347619,0.00001702442,0.0001354009,0.9870629,0.0001657519,0.0001216862,0.01155036],"study_design_scores_gemma":[0.000006891707,0.0001766079,0.00341325,0.000006676258,0.00002139797,0.000251842,0.00001459495,0.002111609,0.9909046,0.00009973352,0.00297846,0.00001447444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3492811,0.006576537,0.6334524,0.0007400283,0.0003239419,0.0009559003,0.001287484,0.002493716,0.004888836],"genre_scores_gemma":[0.4231384,0.003922017,0.5654215,0.0004346121,0.00007891081,0.0003711931,0.0006534233,0.00009458512,0.005885433],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002241643,"threshold_uncertainty_score":0.004902422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04826537179566168,"score_gpt":0.2986839237117031,"score_spread":0.2504185519160415,"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."}}