{"id":"W2101302012","doi":"10.1111/gcb.12672","title":"Carbon accumulation of tropical peatlands over millennia: a modeling approach","year":2014,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"U.S. Forest Service; United States Agency for International Development","keywords":"Peat; Environmental science; Deforestation (computer science); Carbon fibers; Swamp; Carbon cycle; Vegetation (pathology); Litter; Temperate climate; Primary production; Hydrology (agriculture); Ecology; Ecosystem; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001041378,0.0001146156,0.0002222006,0.00001972003,0.00003476562,0.000003658472,0.0001456295,0.0001932687,0.0001587358],"category_scores_gemma":[0.00002259517,0.00008973308,0.00005180605,0.0001127019,0.00009017516,0.00003964744,0.0001411399,0.00006702752,0.00001320712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007580086,"about_ca_system_score_gemma":0.000003388931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00100023,"about_ca_topic_score_gemma":0.0002196876,"domain_scores_codex":[0.9991146,0.00008713451,0.0001744011,0.0002755487,0.00007757706,0.0002707876],"domain_scores_gemma":[0.999695,0.00001597174,0.00005502908,0.0001629567,0.000007338519,0.00006371487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004007316,0.00004286858,0.9968195,0.000004686834,0.000007277593,3.939528e-7,0.00005782455,0.0001421162,0.0001742984,0.0005278756,0.0001206368,0.002062425],"study_design_scores_gemma":[0.0005996967,0.0002405821,0.6306877,0.000002466284,0.0000147276,0.000008248392,0.000008031448,0.3647829,0.000004587489,0.001503582,0.00202376,0.0001237509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809527,0.00006211539,0.005994371,0.0001463642,0.0001263716,0.0001229187,0.00001434185,0.00002471643,0.01255613],"genre_scores_gemma":[0.9990126,0.00002006835,0.0003369321,0.0003011238,0.0001998285,0.00002125611,0.00008983575,0.000004409372,0.0000138806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3661319,"threshold_uncertainty_score":0.3659209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06411985377770586,"score_gpt":0.2990481963111501,"score_spread":0.2349283425334443,"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."}}