{"id":"W2957398446","doi":"10.1021/acs.est.9b02965","title":"Low Greenhouse Gas Emissions from Oyster Aquaculture","year":2019,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Rhode Island Sea Grant, University of Rhode Island; University of Rhode Island; Boston University","keywords":"Greenhouse gas; Aquaculture; Environmental science; Oyster; Fishery; Waste management; Environmental engineering; Environmental protection; Fish <Actinopterygii>; Oceanography; Engineering; Geology; Biology","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":[0.0001838587,0.0005358035,0.0002896434,0.0002033537,0.0003112193,0.0005269173,0.0002416614,0.0002633606,0.0009193316],"category_scores_gemma":[0.0002427065,0.0001420371,0.0003354553,0.0003793394,0.00027407,0.0003129385,0.0005730977,0.000379473,0.000242396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007284384,"about_ca_system_score_gemma":0.0004850783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01181179,"about_ca_topic_score_gemma":0.01643406,"domain_scores_codex":[0.9996716,0.00001886542,0.00001625631,0.0001108107,0.0001322292,0.00005022475],"domain_scores_gemma":[0.9997721,0.00003045058,0.00007289238,0.00002518253,0.00007538288,0.00002388412],"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":[0.0003433687,0.00002758147,0.05083559,0.000232283,0.00004387669,0.0002779062,0.0000842779,0.001351649,0.9396594,0.00009562022,0.0001386302,0.00690977],"study_design_scores_gemma":[0.00002050622,0.0004677304,0.4057902,0.0001008957,0.00009124655,0.0002366794,0.0007084284,0.003798615,0.5818728,0.0002592313,0.006616914,0.00003679076],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950223,0.0004364603,0.0009831015,0.00003337624,0.00001038423,0.00001073667,0.000820786,0.00003118793,0.002651681],"genre_scores_gemma":[0.9948862,0.0005295996,0.001086461,0.00006473389,0.000003910847,0.00002437148,0.001031727,0.00002409687,0.002348819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01181179,"threshold_uncertainty_score":0.02348608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00432587925613967,"score_gpt":0.2066638740073027,"score_spread":0.202337994751163,"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."}}