{"id":"W1980247664","doi":"10.1007/s00216-011-5376-6","title":"Triacylglycerol profiling of microalgae strains for biofuel feedstock by liquid chromatography–high-resolution mass spectrometry","year":2011,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Algal biology and biofuel production","field":"Energy","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Marine Biosciences","funders":"","keywords":"Chromatography; Chemistry; Mass spectrometry; Orbitrap; Gas chromatography; Fatty acid methyl ester; Biofuel; Oleic acid; Derivatization; Biodiesel; Biochemistry; Biotechnology; Biology","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":[0.0003871839,0.0006052093,0.0004824842,0.0008334813,0.0003533109,0.0005867056,0.0002584833,0.0003451204,0.0003269138],"category_scores_gemma":[0.0004728143,0.0002265609,0.0003725296,0.0006413388,0.0002121178,0.0003140946,0.0003587424,0.0004190259,0.0004536224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003310652,"about_ca_system_score_gemma":0.0003538192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585634,"about_ca_topic_score_gemma":0.003460653,"domain_scores_codex":[0.99963,0.00004786881,0.00004405476,0.00006968751,0.0001610634,0.00004719787],"domain_scores_gemma":[0.9998186,0.00004767345,0.00002947815,0.00001886573,0.0000617953,0.00002366051],"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":[0.0000304895,0.000009899607,0.0002804126,0.000008695783,0.000003219503,0.00001551375,0.00001032393,0.00003856985,0.9987273,0.000007760967,0.000007667542,0.0008601994],"study_design_scores_gemma":[0.000008412169,0.000120829,0.00927572,0.000006844396,0.00003159075,0.0001351559,0.00006088807,0.0009332814,0.9886004,0.00003687624,0.0007797017,0.00001033029],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830993,0.001789629,0.01187452,0.0001841821,0.00003093577,0.0001381245,0.001578389,0.000189502,0.001115326],"genre_scores_gemma":[0.9428619,0.00258709,0.04664173,0.0002450474,0.00001823247,0.000285039,0.004518514,0.0001006783,0.002741795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001585634,"threshold_uncertainty_score":0.003152847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01935927869561359,"score_gpt":0.2355998365177027,"score_spread":0.2162405578220891,"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."}}