{"id":"W2523691629","doi":"10.1007/s11306-016-1111-9","title":"Coupling solid phase microextraction to complementary separation platforms for metabotyping of E. coli metabolome in response to natural antibacterial agents","year":2016,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Metabolome; Metabolomics; Eugenol; Chromatography; Chemistry; Metabolite; Essential oil; Solid-phase microextraction; Central composite design; Factorial experiment; Food science; Mass spectrometry; Gas chromatography–mass spectrometry; Biochemistry; Response surface methodology","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":[0.0003188468,0.0005494006,0.0004068961,0.0003354281,0.0002128441,0.0005448114,0.0003428106,0.0006924437,0.0006637391],"category_scores_gemma":[0.0005931844,0.0003729079,0.0003393112,0.0003074035,0.000228545,0.0003880136,0.0005647549,0.0007094402,0.0005874654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002110492,"about_ca_system_score_gemma":0.0003774786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004606483,"about_ca_topic_score_gemma":0.002136065,"domain_scores_codex":[0.9996144,0.00007868046,0.00002284105,0.0001092762,0.000124793,0.00005001783],"domain_scores_gemma":[0.9996669,0.0001340059,0.00004669597,0.00003360794,0.00008497949,0.00003383685],"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":[0.00002957286,0.00001277331,0.00007503124,0.00001255871,0.000003645166,0.000005220463,0.000004237344,0.00002121387,0.9989967,0.00001160015,0.000009204596,0.0008182515],"study_design_scores_gemma":[0.000005063924,0.0001421494,0.001750821,0.000004415709,0.00001299041,0.00003961462,0.00001752488,0.0009767605,0.9963343,0.00005061402,0.0006589023,0.000006827447],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8953836,0.001658936,0.09885447,0.0004139808,0.0001883622,0.0003071909,0.001373067,0.000319734,0.001500577],"genre_scores_gemma":[0.78917,0.002726276,0.1991621,0.0004259124,0.00007766009,0.0004985479,0.002262822,0.00008565443,0.005590929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006924437,"threshold_uncertainty_score":0.002220392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0272171710640874,"score_gpt":0.3622910079423436,"score_spread":0.3350738368782562,"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."}}