{"id":"W4411987460","doi":"10.1016/j.jclepro.2025.146101","title":"Decoding carbon emissions in China's marine Fisheries: Trends, drivers, and pathways to sustainability","year":2025,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission; National Key Research and Development Program of China; National Natural Science Foundation of China; Shanghai Ocean University; Canadian Engineering Memorial Foundation; State Key Laboratory of Resources and Environmental Information System; Shanghai Lingjun Program","keywords":"Sustainability; China; Fishery; Environmental science; Carbon fibers; Greenhouse gas; Business; Natural resource economics; Ecology; Geography; Economics; Computer science; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007261673,0.0001119213,0.0001825602,0.000154603,0.00008872763,0.00002370388,0.0001063601,0.00005179215,0.0002040309],"category_scores_gemma":[0.0005408582,0.00009535145,0.00004509757,0.0003725749,0.0001240278,0.0003296689,0.0002150743,0.0002263609,5.972182e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110294,"about_ca_system_score_gemma":0.00002856211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003646695,"about_ca_topic_score_gemma":0.00021479,"domain_scores_codex":[0.9989458,0.00008986038,0.0003412578,0.0002274742,0.0001917718,0.0002038618],"domain_scores_gemma":[0.9995493,0.00001870826,0.0001073245,0.0001876521,0.0000166045,0.0001203563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001243668,0.0001472966,0.8698681,0.00002675264,0.000005952828,0.00001297481,0.001765419,0.002780084,0.002290559,0.00001002403,0.0009823237,0.1219861],"study_design_scores_gemma":[0.0002583629,0.0001396925,0.9862662,0.00001933881,0.00001394859,0.00002234823,0.002848299,0.0001257711,0.001834848,0.002571397,0.005806085,0.0000936884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889619,0.00001803624,0.00003392492,0.006773608,0.0001837343,0.0001486088,4.349696e-7,0.000007565377,0.003872237],"genre_scores_gemma":[0.9975583,0.00003887443,0.0004252855,0.00005688808,0.00005115673,0.000002721783,6.347795e-7,0.000005554605,0.001860589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1218924,"threshold_uncertainty_score":0.3888319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005241844503559996,"score_gpt":0.2351979735757529,"score_spread":0.229956129072193,"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."}}