{"id":"W2011893747","doi":"10.1016/j.chroma.2012.06.052","title":"Solid phase microextraction coupled with comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry for high-resolution metabolite profiling in apples: Implementation of structured separations for optimization of sample preparation procedure in complex samples","year":2012,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Chromatography; Mass spectrometry; Sample preparation; Metabolite; Solid-phase microextraction; Resolution (logic); Metabolomics; Metabolite profiling; Gas chromatography–mass spectrometry; Time-of-flight mass spectrometry; Gas chromatography; Organic chemistry; Biochemistry","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":[0.0003852278,0.0005259098,0.0005333778,0.0002489717,0.0003731597,0.0005340152,0.0004481744,0.0004922729,0.000405136],"category_scores_gemma":[0.000303816,0.0004092154,0.0004466754,0.0002517855,0.00026873,0.0004742744,0.0005201892,0.0006773311,0.0003184832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003621524,"about_ca_system_score_gemma":0.0008214974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009795625,"about_ca_topic_score_gemma":0.005984181,"domain_scores_codex":[0.9996274,0.00004590974,0.00002059016,0.0001298188,0.0001450393,0.00003125122],"domain_scores_gemma":[0.9998524,0.00004472724,0.00001851298,0.00002363136,0.00003880223,0.00002191413],"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":[0.0000544737,0.00002401613,0.0001383824,0.00001687156,0.00000721575,0.00001171741,0.00000738481,0.00009227241,0.9967747,0.00002550875,0.00002529246,0.002822067],"study_design_scores_gemma":[0.00001220713,0.0001548816,0.002886857,0.000003475218,0.00002852491,0.00008856808,0.000009472747,0.003415128,0.9924994,0.00004836169,0.0008409422,0.00001228945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8852594,0.002971036,0.1084224,0.0003075433,0.0001243798,0.0002174426,0.0009555021,0.000609304,0.001132805],"genre_scores_gemma":[0.7514617,0.002524567,0.2408319,0.0002468671,0.00004925222,0.0001913875,0.001148199,0.0001012694,0.003444764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009795625,"threshold_uncertainty_score":0.002627671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769619043213949,"score_gpt":0.3349938815271595,"score_spread":0.31729769109502,"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."}}