{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001650859,0.0002875305,0.0002524089,0.000125967,0.000453855,0.00004004337,0.001075146,0.0001899252,0.01256971],"category_scores_gemma":[0.00002929431,0.0002142553,0.00008858953,0.0008426576,0.002254807,0.0004215354,0.001770931,0.0003398865,0.00957943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003399893,"about_ca_system_score_gemma":0.00001059062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001515348,"about_ca_topic_score_gemma":0.00007222516,"domain_scores_codex":[0.9974666,0.00001971521,0.0002465281,0.0009437335,0.0006529333,0.000670477],"domain_scores_gemma":[0.9990062,0.00001953767,0.0001068759,0.0006939035,0.000002272762,0.0001712526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007512243,0.0001719623,0.3085575,0.000001666091,0.000009806108,0.00001115309,0.0003454364,0.00004018312,0.6755785,0.0001287041,0.001743748,0.01340391],"study_design_scores_gemma":[0.001238351,0.0005515832,0.3274435,0.00005279903,0.00004804387,0.00005526693,0.003523403,0.0003600506,0.2644813,0.004727798,0.3962336,0.001284289],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708958,0.00007505478,0.00004291827,0.0015311,0.0001858377,0.0003329211,0.00002202164,0.0001814821,0.02673287],"genre_scores_gemma":[0.9923272,0.00007903102,0.0009070563,0.0004103715,0.00003597837,0.00002577433,0.00001173767,0.00001808222,0.006184798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4110972,"threshold_uncertainty_score":0.9911917,"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."}}