{"id":"W2910877143","doi":"10.22092/ijfs.2018.117675","title":"Effects of different cooking methods on minerals, vitamins and nutritional quality indices of grass carp (Ctenopharyngodon idella)","year":2019,"lang":"en","type":"article","venue":"AquaDocs  (United Nations Educational, Scientific and Cultural Organization)","topic":"Aquaculture Nutrition and Growth","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Organization for Security and Co-operation in Europe; Instituto Colombiano de Bienestar Familiar; Sveriges Regering; Inter-American Development Bank; Japan International Cooperation Agency; European Commission; Canadian Institute for Theoretical Astrophysics","keywords":"Grass carp; Food science; Chemistry; Proximate; Vitamin; Cooking methods; Fatty acid; Vitamin C; Fish <Actinopterygii>; Biology; Fishery; Biochemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000267456,0.0001764038,0.000253352,0.0003969425,0.0004962832,0.0001253664,0.0001687257,0.0001050019,0.0003614415],"category_scores_gemma":[0.0004392338,0.00008782728,0.00005363123,0.00439328,0.0002414269,0.0003333118,0.00005127321,0.0001047289,0.00001206356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002889942,"about_ca_system_score_gemma":0.00002373681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007353428,"about_ca_topic_score_gemma":0.00006391563,"domain_scores_codex":[0.998447,0.0002273011,0.0003960009,0.0003793863,0.0003970015,0.0001533125],"domain_scores_gemma":[0.9974502,0.001062145,0.0003166987,0.00007258738,0.0009879684,0.000110346],"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":[0.00001247006,0.0006142457,0.03328501,0.0001742734,0.00004192466,1.168463e-7,0.0006213869,0.000005047122,0.8478106,0.1150321,0.001625928,0.0007768334],"study_design_scores_gemma":[0.0006806607,0.0001633935,0.8829086,0.000172213,0.00006415851,0.000008485616,0.001786485,0.00005971774,0.103154,0.003075414,0.007601408,0.0003254329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955757,0.0003515295,0.00003090017,0.002904081,0.0002125742,0.0003963908,0.0001625605,0.00002229581,0.0003439152],"genre_scores_gemma":[0.9937056,0.0001862491,0.0007696005,0.0001726971,0.00008109373,0.00001401638,0.003132471,0.000002687082,0.00193563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8496236,"threshold_uncertainty_score":0.3957531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614269064030867,"score_gpt":0.2842140299950786,"score_spread":0.2680713393547699,"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."}}