{"id":"W3081481783","doi":"10.1093/icesjms/fsaa121","title":"State of art and best practices for fatty acid analysis in aquatic sciences","year":2020,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Aquaculture Nutrition and Growth","field":"Agricultural and Biological Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Fundação para a Ciência e a Tecnologia; Ministério da Ciência, Tecnologia e Ensino Superior; Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro; Université de Bretagne Occidentale; Centre National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Trophic level; Standardization; Environmental science; Derivatization; Sample (material); Fatty acid; Fish <Actinopterygii>; Ecology; Computer science; Chemistry; Biology; Fishery; Chromatography; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237552,0.00007155345,0.0002300948,0.00007962426,0.0001272596,0.0001055597,0.0004051038,0.00001670554,0.00004214167],"category_scores_gemma":[0.0007645885,0.00002672342,0.00007495259,0.002222227,0.0004593773,0.0007330792,0.0001061448,0.00008441903,0.00000151986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008536387,"about_ca_system_score_gemma":0.00003253172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000879773,"about_ca_topic_score_gemma":0.0006802293,"domain_scores_codex":[0.9988534,0.00004176013,0.0003477993,0.0001823079,0.0004149059,0.0001598534],"domain_scores_gemma":[0.9986297,0.0002573597,0.0007960958,0.00002023541,0.0001674132,0.0001291318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002098993,0.0002865928,0.3030196,0.00004919497,0.00005463424,0.000008979699,0.0007182151,0.0001426782,0.5617782,0.0002047909,0.0001157377,0.1334115],"study_design_scores_gemma":[0.001332697,0.007307784,0.8565308,0.000125718,0.0003841745,0.00005798121,0.00434312,0.01080391,0.1021772,0.006058815,0.01037213,0.0005056813],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919429,0.0001358622,0.00008795294,0.006912867,0.00002127849,0.00008279399,0.000007330631,0.000002807813,0.0008062357],"genre_scores_gemma":[0.9958839,0.0001586158,0.003653161,0.0002227618,0.0000526364,0.000001011184,0.000001126812,2.470656e-7,0.0000265581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5535112,"threshold_uncertainty_score":0.1692595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05576293778000367,"score_gpt":0.3053410924170653,"score_spread":0.2495781546370616,"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."}}