{"id":"W4304784517","doi":"10.3389/fmars.2022.981569","title":"Assessing the carbon sink capacity of coastal mariculture shellfish resources in China from 1981–2020","year":2022,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"National Key Research and Development Program of China","keywords":"Mariculture; Shellfish; Fishery; Carbon sink; Environmental science; Sink (geography); Aquaculture; Climate change; Ecology; Geography; Aquatic animal; 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":[],"consensus_categories":[],"category_scores_codex":[0.001150909,0.0001739168,0.0002604583,0.0000818701,0.0004548707,0.00007971038,0.001064022,0.00003144194,0.0003407044],"category_scores_gemma":[0.0001691818,0.0001213436,0.00005811478,0.001908769,0.00128939,0.0003226422,0.003572584,0.0004985005,0.000001264564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002675116,"about_ca_system_score_gemma":0.00003116399,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01628762,"about_ca_topic_score_gemma":0.002983788,"domain_scores_codex":[0.9976708,0.0001728374,0.0003140576,0.0005362341,0.0009053656,0.0004007279],"domain_scores_gemma":[0.9994147,0.00004259503,0.0001567348,0.0003184683,0.00001012009,0.00005735444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001226794,0.00007350758,0.9826352,0.000003338075,0.000004112494,0.00001171904,0.003901948,0.001531872,0.003913329,0.00001307817,0.001849238,0.006050387],"study_design_scores_gemma":[0.000228247,0.00003682532,0.9858893,0.000008102464,0.000007151051,0.000003275942,0.005072622,0.001977596,0.0003800762,0.001911191,0.00431795,0.000167736],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9576699,0.00003017216,0.0000191764,0.0008659583,0.0004341204,0.0001968432,0.00001334704,0.00001445104,0.04075604],"genre_scores_gemma":[0.993185,0.00001755384,0.006050693,0.0001356807,0.00004183315,0.00002333515,0.000006692318,0.000006772841,0.0005324783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04022356,"threshold_uncertainty_score":0.990263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006661059684067448,"score_gpt":0.216933187018222,"score_spread":0.2102721273341545,"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."}}