{"id":"W4235891100","doi":"10.17760/d20002669","title":"The integration of mass spectrometry and NMR for structural characterization of trace-level analytes in complex mixtures","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Mass spectrometry; Characterization (materials science); Analyte; Chemistry; Nuclear magnetic resonance spectroscopy; Analytical Chemistry (journal); Materials science; Chromatography; Nanotechnology; Organic chemistry","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":[0.001066631,0.0006285593,0.0006592909,0.001006113,0.0004226938,0.001289291,0.0005903468,0.0007363587,0.001914047],"category_scores_gemma":[0.0007788679,0.0004131979,0.0004476108,0.0005682058,0.000576481,0.001215763,0.0007019276,0.001482195,0.001351952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005013941,"about_ca_system_score_gemma":0.0007860291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000334003,"about_ca_topic_score_gemma":0.0008510031,"domain_scores_codex":[0.9994161,0.00008234019,0.00002853098,0.0001267585,0.0002932898,0.00005302687],"domain_scores_gemma":[0.9996644,0.0001479258,0.00003595467,0.00003655886,0.0000937099,0.00002142488],"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":[0.00008772269,0.000132909,0.0004967914,0.0008759666,0.00004135345,0.0001563739,0.00024301,0.001560795,0.8313581,0.009653445,0.001425177,0.1539684],"study_design_scores_gemma":[0.00001622185,0.0005055135,0.001853112,0.0003119747,0.00007278182,0.0008823242,0.0001836437,0.005472946,0.8930881,0.005536815,0.09200896,0.00006758124],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1809675,0.1331488,0.6191234,0.005108998,0.001435771,0.0007162151,0.001166524,0.00128948,0.05704334],"genre_scores_gemma":[0.2910215,0.1692196,0.4989502,0.00169906,0.0009171065,0.0006401349,0.0007028146,0.0002553603,0.03659416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001914047,"threshold_uncertainty_score":0.006403148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02436636750244358,"score_gpt":0.2811290733749404,"score_spread":0.2567627058724968,"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."}}