{"id":"W2029921874","doi":"10.1016/j.aquaculture.2012.02.018","title":"The effect of increasing inclusion rates of soybean, pea and canola meals and their protein concentrates on the growth of rainbow trout: Concepts in diet formulation and experimental design for ingredient evaluation","year":2012,"lang":"en","type":"article","venue":"Aquaculture","topic":"Aquaculture Nutrition and Growth","field":"Agricultural and Biological Sciences","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Canola; Ingredient; Biology; Soybean meal; Fish meal; Meal; Animal science; Feed conversion ratio; Soy protein; Food science; Rainbow trout; Nutrient; Plant protein; Fish <Actinopterygii>; Body weight; Fishery; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001393003,0.0001232244,0.0001905023,0.000008386145,0.0002668449,0.00001776961,0.00006331193,0.00007859158,0.000003183863],"category_scores_gemma":[0.000303487,0.00003364014,0.00003392319,0.0001040195,0.0001471311,0.0001240847,0.00006021709,0.00005846336,3.412642e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000181337,"about_ca_system_score_gemma":0.000004053997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001395185,"about_ca_topic_score_gemma":0.00005430784,"domain_scores_codex":[0.9989066,0.000464319,0.0002142808,0.000120481,0.0001584485,0.0001358499],"domain_scores_gemma":[0.9986324,0.001012148,0.0001970149,0.00002782699,0.00009437541,0.00003624502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009580354,0.00005004345,0.0149963,0.00003569981,0.0000145725,2.557198e-8,0.005993319,9.453354e-7,0.9747977,0.001261572,0.00003974188,0.001852005],"study_design_scores_gemma":[0.001027148,0.001492781,0.04678731,0.0001601359,0.00001781234,0.000001566223,0.003589993,0.0004880583,0.9454693,0.0008347565,0.00004821933,0.00008294806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953755,0.002509065,0.000004450547,0.0005139724,0.00001116999,0.001529616,0.00002794095,0.000004103558,0.00002420243],"genre_scores_gemma":[0.9997407,0.00003846654,0.00004329683,0.0000291857,0.000033612,0.00007961573,0.00003072758,0.000001097345,0.000003287748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03179102,"threshold_uncertainty_score":0.2052382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0285602624190245,"score_gpt":0.2855954868775997,"score_spread":0.2570352244585752,"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."}}