{"id":"W2115583562","doi":"10.1016/j.jfca.2009.01.011","title":"Quality control of nutrient data entry for a long-term, multi-centre dietary intervention trial","year":2009,"lang":"en","type":"article","venue":"Journal of Food Composition and Analysis","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network","funders":"","keywords":"Nutrient; Term (time); Food composition data; Control (management); Intervention (counseling); Environmental science; Computer science; Medicine; Food science; Chemistry; Biology; Ecology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08840558,0.001545635,0.005823846,0.00139912,0.002648582,0.004859757,0.002392515,0.005804312,0.002637484],"category_scores_gemma":[0.1385314,0.001841537,0.004993437,0.002442739,0.0029354,0.00332801,0.002960857,0.004131564,0.0003472274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004586998,"about_ca_system_score_gemma":0.00691255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004921936,"about_ca_topic_score_gemma":0.004872196,"domain_scores_codex":[0.8703324,0.09383138,0.01525854,0.008736761,0.008894159,0.0029468],"domain_scores_gemma":[0.8710657,0.06200663,0.03625184,0.01408877,0.01051644,0.006070626],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.936453,0.003756682,0.02036597,0.001232386,0.009812506,0.00004162703,0.0004242007,0.0005687476,0.0007522565,0.000205197,0.001173585,0.02521379],"study_design_scores_gemma":[0.7370508,0.08076684,0.1410548,0.001089861,0.02663188,0.0001602184,0.0002648787,0.007146057,0.002055573,0.00131844,0.002165299,0.0002952311],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474616,0.01806418,0.01506253,0.004178696,0.001639378,0.009909653,0.001335909,0.0005066699,0.001841348],"genre_scores_gemma":[0.9899907,0.0004778289,0.004580436,0.0007068473,0.000259684,0.003338516,0.0004198625,0.00003792545,0.0001881982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9115944,"threshold_uncertainty_score":0.467539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06684025170713312,"score_gpt":0.372114256075832,"score_spread":0.3052740043686989,"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."}}