{"id":"W2789555197","doi":"10.1186/s12263-018-0592-8","title":"Guidelines for Biomarker of Food Intake Reviews (BFIRev): how to conduct an extensive literature search for biomarker of food intake discovery","year":2018,"lang":"en","type":"review","venue":"Genes & Nutrition","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; China Scholarship Council; Sapienza Università di Roma; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; Agència de Gestió d'Ajuts Universitaris i de Recerca; Ministerio de Economía y Competitividad; Science Foundation Ireland; Carlsbergfondet; Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable; World Health Organization","keywords":"Biomarker; Identification (biology); Biomarker discovery; Food intake; Guideline; Metabolomics; Data science; Medicine; Biology; Computer science; Bioinformatics; Pathology; Proteomics; Ecology; Internal medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06380689,0.002458295,0.004959611,0.02271702,0.002272327,0.008095399,0.007590664,0.01044891,0.02441944],"category_scores_gemma":[0.1617418,0.002067216,0.006726327,0.01760321,0.002544774,0.01019079,0.00682926,0.005421819,0.02544221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004711281,"about_ca_system_score_gemma":0.0358351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006915788,"about_ca_topic_score_gemma":0.01335415,"domain_scores_codex":[0.939649,0.02037412,0.02507075,0.001858596,0.01192744,0.001120011],"domain_scores_gemma":[0.8159591,0.07927411,0.02109626,0.006916845,0.07331943,0.003434249],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001013384,0.00008244683,0.0004202904,0.1055303,0.0002926764,0.0003442511,0.001021023,0.0002420286,0.0007685201,0.009805001,0.5085723,0.3728198],"study_design_scores_gemma":[0.00007475528,0.00003401221,0.0006603652,0.08993815,0.000304943,0.000300526,0.0002913568,0.00009296882,0.0002710923,0.006288229,0.9016815,0.0000620283],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.0009683796,0.6049169,0.06246163,0.1676451,0.02753909,0.03367372,0.02933477,0.004405335,0.06905503],"genre_scores_gemma":[0.005257849,0.462101,0.3684887,0.06507698,0.004479818,0.04323286,0.02090458,0.001354434,0.02910379],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9361931,"threshold_uncertainty_score":0.3374471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.198997445430995,"score_gpt":0.4048326201692362,"score_spread":0.2058351747382412,"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."}}