{"id":"W2913010965","doi":"10.1111/jwas.12595","title":"The future of genetic engineering to provide essential dietary nutrients and improve growth performance in aquaculture: Advantages and challenges","year":2019,"lang":"en","type":"article","venue":"Journal of the World Aquaculture Society","topic":"Aquaculture Nutrition and Growth","field":"Agricultural and Biological Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Aquaculture; Biotechnology; Biology; Sustainability; Fish meal; Business; Natural resource economics; Fishery; Fish <Actinopterygii>; Ecology; Economics","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":[0.0003093741,0.0001981315,0.0002891434,0.00001328892,0.0001730849,0.00005743768,0.0003520309,0.0001075024,0.000006516471],"category_scores_gemma":[0.00002600774,0.00005780099,0.0002147917,0.0003413465,0.0000743514,0.0001973269,0.000133357,0.0004256389,0.00000105328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003017776,"about_ca_system_score_gemma":0.000008533715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007095075,"about_ca_topic_score_gemma":0.0002222978,"domain_scores_codex":[0.9987845,0.00006090488,0.0003848585,0.0001966798,0.0003303258,0.000242721],"domain_scores_gemma":[0.9992862,0.0001067931,0.0002935291,0.00006774542,0.0001405045,0.0001052688],"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":[0.0004652066,0.000367471,0.1208073,0.0007226885,0.00029492,0.000006087937,0.008430605,0.00005865108,0.810674,0.001239505,0.005496729,0.0514369],"study_design_scores_gemma":[0.0009536675,0.0004530739,0.8380438,0.0003850023,0.00006061526,0.00006092465,0.006019922,0.00009715027,0.01097035,0.0002327093,0.1424129,0.0003099022],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669979,0.01329378,5.929548e-7,0.01901602,0.0002602839,0.0003536509,0.00001433876,0.000007887248,0.00005561385],"genre_scores_gemma":[0.985082,0.01324542,0.0004117226,0.0003091455,0.0005120907,0.000005308353,0.000001666342,0.000002595554,0.0004300547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7997036,"threshold_uncertainty_score":0.2357056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005770291809545462,"score_gpt":0.1972084130563092,"score_spread":0.1914381212467637,"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."}}