{"id":"W4408824541","doi":"10.5194/oos2025-229","title":"A Traits-based assessment of aquaculture’s sustainable development potential at the food-climate-biodiversity nexus","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Food Industry and Aquatic Biology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"","keywords":"Biodiversity; Nexus (standard); Aquaculture; Sustainable development; Climate change; Environmental resource management; Fishery; Geography; Natural resource economics; Business; Environmental planning; Environmental science; Ecology; Biology; Fish <Actinopterygii>; Economics; Engineering","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":[0.001659858,0.0005671859,0.000216294,0.002942594,0.0003293859,0.001280484,0.0003548997,0.0004153918,0.001282751],"category_scores_gemma":[0.003649255,0.0001324397,0.0006942053,0.001876985,0.0005996284,0.001001969,0.001334509,0.0004321138,0.0002433584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007411728,"about_ca_system_score_gemma":0.0004637356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003606163,"about_ca_topic_score_gemma":0.005472806,"domain_scores_codex":[0.9992868,0.000251172,0.00006228972,0.0001302589,0.000197748,0.00007174208],"domain_scores_gemma":[0.9976186,0.000885541,0.0007953469,0.0001635912,0.0003800535,0.0001567302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008655128,0.0001432889,0.9192572,0.0001131337,0.0002820458,0.0001651788,0.0005590082,0.02946876,0.006241584,0.004015517,0.0004228351,0.0392449],"study_design_scores_gemma":[0.000007460717,0.0003637233,0.8646308,0.00006952008,0.00009390122,0.0002122129,0.001803827,0.1232867,0.002038535,0.005644053,0.001806628,0.00004264431],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711003,0.0001391222,0.02092245,0.0001766958,0.000006620303,0.00005387971,0.0008349504,0.000050045,0.0067159],"genre_scores_gemma":[0.9947187,0.00003575325,0.004706359,0.00001183327,0.00000281848,0.00002546063,0.0002512826,0.000002597789,0.0002451572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003606163,"threshold_uncertainty_score":0.008778274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03043093932320838,"score_gpt":0.2493385232084448,"score_spread":0.2189075838852364,"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."}}