{"id":"W2969812248","doi":"10.1111/raq.12374","title":"Mapping diversity of species in global aquaculture","year":2019,"lang":"en","type":"article","venue":"Reviews in Aquaculture","topic":"Aquaculture Nutrition and Growth","field":"Agricultural and Biological Sciences","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"","keywords":"Aquaculture; Diversification (marketing strategy); Diversity index; Sustainability; Diversity (politics); Profitability index; Ecosystem diversity; Biodiversity; Species diversity; Agriculture; Ecology; Production (economics); Environmental resource management; Fish farming; Natural resource economics; Geography; Fishery; Business; Biology; Species richness; Fish <Actinopterygii>; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001018061,0.0003441897,0.0002452673,0.00834404,0.0001775266,0.0007970326,0.0001935571,0.0001539315,0.001147611],"category_scores_gemma":[0.001358195,0.00008342194,0.0003270463,0.009877462,0.00033248,0.0007453791,0.0009691534,0.0001753261,0.0002255447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003219988,"about_ca_system_score_gemma":0.0003968424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00195363,"about_ca_topic_score_gemma":0.002230907,"domain_scores_codex":[0.9993014,0.000177712,0.00006437027,0.0001945106,0.0002012629,0.00006078248],"domain_scores_gemma":[0.9991056,0.0002405073,0.0003455255,0.00005539317,0.0001920646,0.00006076212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001876184,0.00004184189,0.5720927,0.003401513,0.0007595757,0.0002967132,0.002672762,0.007186705,0.01261163,0.004602484,0.004280403,0.3918661],"study_design_scores_gemma":[0.000005507057,0.0001166198,0.9661821,0.0004879104,0.0001308768,0.0003091944,0.002392533,0.002008413,0.001645938,0.002403158,0.02428614,0.00003147143],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.9323646,0.02813523,0.0119706,0.0002946405,0.00007377713,0.00006773006,0.01132016,0.0001180377,0.01565519],"genre_scores_gemma":[0.9800671,0.006631455,0.007226637,0.00004270031,0.00003072639,0.00005711105,0.005085806,0.00001769503,0.0008407757],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00834404,"threshold_uncertainty_score":0.005384088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349994730925821,"score_gpt":0.2481224162112975,"score_spread":0.2131229431187154,"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."}}