{"id":"W2612023115","doi":"10.1016/j.marpol.2017.05.007","title":"Solutions to blue carbon emissions: Shrimp cultivation, mangrove deforestation and climate change in coastal Bangladesh","year":2017,"lang":"en","type":"article","venue":"Marine Policy","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":106,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Manitoba","funders":"Leibniz-Zentrum für Marine Tropenforschung; Alexander von Humboldt-Stiftung","keywords":"Mangrove; Blue carbon; Deforestation (computer science); Shrimp; Environmental science; Shrimp farming; Greenhouse gas; Climate change; Carbon sequestration; Agroforestry; Penaeus monodon; Ecosystem; Fishery; Environmental protection; Aquaculture; Ecology; Carbon dioxide; 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":[0.0001747697,0.0001214532,0.0001216735,0.0001020566,0.0002933286,0.00008211488,0.0001684123,0.00005064458,0.00007546196],"category_scores_gemma":[0.0001826416,0.0001168184,0.00001998905,0.000133364,0.00005659354,0.0002511405,0.001324497,0.00007435222,0.00005592216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001890877,"about_ca_system_score_gemma":0.00001017288,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08480792,"about_ca_topic_score_gemma":0.25572,"domain_scores_codex":[0.9990538,0.00002336474,0.0001803198,0.0002428799,0.0001356906,0.0003639108],"domain_scores_gemma":[0.9994215,0.00001926587,0.00008928869,0.000313318,0.000007551861,0.0001490359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002011519,0.00003484639,0.9683467,0.00001945739,0.000002042885,0.000005554356,0.001012752,0.0001236873,0.0003522575,0.001133383,0.00006692461,0.02888229],"study_design_scores_gemma":[0.0003151057,0.00004217835,0.9788817,0.00003250803,0.000003783316,0.000009127019,0.00007915061,0.01913024,0.0000260663,0.0005532164,0.0007901174,0.0001367912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824647,0.000002628609,0.00003460536,0.003014687,0.00005764964,0.000369237,0.00005134583,0.0000275403,0.01397765],"genre_scores_gemma":[0.998944,0.00003853605,0.0002654255,0.0001599779,0.000105333,0.00008160641,0.00003934998,0.00001481515,0.0003509288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.170912,"threshold_uncertainty_score":0.9212864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747573927425607,"score_gpt":0.271674795487726,"score_spread":0.2541990562134699,"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."}}