{"id":"W2608908776","doi":"10.5194/bg-14-5507-2017","title":"Coupled eco-hydrology and biogeochemistry algorithms enable the simulation of water table depth effects on boreal peatland net CO <sub>2</sub> exchange","year":2017,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge; University of Alberta","funders":"Natural Environment Research Council; Natural Sciences and Engineering Research Council of Canada; Sight Research UK; Compute Canada; Canadian Foundation for Climate and Atmospheric Sciences; Western Canada Research Grid; University of Alberta; BIOCAP Canada","keywords":"Peat; Biogeochemistry; Primary production; Environmental science; Boreal; Water table; Hydrology (agriculture); Biogeochemical cycle; Ecosystem; Ecology; Geology; Groundwater; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005141125,0.0001576985,0.0002062541,0.00003300438,0.000737738,0.00009319162,0.0004366655,0.000116118,0.00008875217],"category_scores_gemma":[0.00006432005,0.00008601863,0.00003568666,0.00006706324,0.0008938938,0.0001778413,0.0002272579,0.0000793629,0.0000263751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000244201,"about_ca_system_score_gemma":0.00001167854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00224337,"about_ca_topic_score_gemma":0.001076289,"domain_scores_codex":[0.9987286,0.00004951267,0.0001620565,0.0003994357,0.0002347027,0.0004256481],"domain_scores_gemma":[0.9992244,0.000162086,0.0001475022,0.0003629799,0.00001019376,0.00009287207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000521708,0.00007122441,0.7337983,0.00002409648,0.00001219771,0.00001320833,0.0002639489,0.0004442269,0.2569656,0.000006164658,0.0007863724,0.007562469],"study_design_scores_gemma":[0.000819698,0.00055814,0.5388364,0.00001374326,0.00002626352,0.00001411896,0.00002728816,0.04455524,0.4089208,0.0005114999,0.00548912,0.0002276915],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955804,0.00005318954,0.0000413237,0.0003873207,0.000160453,0.0002037111,0.00003284651,0.00001453158,0.003526193],"genre_scores_gemma":[0.9994276,0.0001187594,0.00002579897,0.0001421415,0.00009816307,0.0000208113,0.00005454549,0.000005847251,0.0001062974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1949619,"threshold_uncertainty_score":0.5674159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043766930184052,"score_gpt":0.2351856690931137,"score_spread":0.2247479997912732,"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."}}