{"id":"W2769572656","doi":"10.1021/acs.analchem.7b03241","title":"Environmental Nuclear Magnetic Resonance Spectroscopy: An Overview and a Primer","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Krembil Foundation; Canada Foundation for Innovation","keywords":"Chemistry; Spectroscopy; Nuclear magnetic resonance spectroscopy; Nuclear magnetic resonance; Environmental chemistry; Perspective (graphical); Physics; Artificial intelligence; Organic chemistry; Computer science","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":[0.002009399,0.001555984,0.001515517,0.004541641,0.0005313149,0.002010412,0.001415546,0.003914244,0.003674254],"category_scores_gemma":[0.001437583,0.001033111,0.000697232,0.003546276,0.001229818,0.005190521,0.001249432,0.005402082,0.007063127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008712889,"about_ca_system_score_gemma":0.001066743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009696886,"about_ca_topic_score_gemma":0.00140332,"domain_scores_codex":[0.9995481,0.0001023232,0.00007086882,0.00008252772,0.0001543634,0.00004183151],"domain_scores_gemma":[0.9988921,0.0005357558,0.0001333888,0.0000401055,0.0002881552,0.0001105365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000208029,0.0004264089,0.0007937578,0.01056018,0.00009927117,0.0007574177,0.0002363297,0.0009706136,0.01109026,0.02255701,0.1860873,0.7662134],"study_design_scores_gemma":[0.000008850081,0.0001263294,0.000522002,0.00163099,0.00002119935,0.002167769,0.00007732311,0.0001544425,0.0007801836,0.006659557,0.9878091,0.00004217148],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002223366,0.9889237,0.002167749,0.003620177,0.001905252,0.00002447552,0.00006541861,0.00003975482,0.003031019],"genre_scores_gemma":[0.0007909585,0.9852887,0.003177994,0.003123131,0.005055836,0.00005912782,0.0001263375,0.00002198614,0.002356037],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004541641,"threshold_uncertainty_score":0.01229167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408508333855222,"score_gpt":0.3209145667910464,"score_spread":0.3068294834524942,"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."}}