{"id":"W2582182655","doi":"10.1111/gcb.13638","title":"Direct and indirect climate change effects on carbon dioxide fluxes in a thawing boreal forest–wetland landscape","year":2017,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Lethbridge; Université de Montréal; Center for Northern Studies","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutscher Akademischer Austauschdienst; Canada Foundation for Innovation","keywords":"Wetland; Environmental science; Climate change; Boreal; Taiga; Carbon dioxide; Peat; Atmospheric sciences; Hydrology (agriculture); Physical geography; Ecology; Forestry; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000236313,0.0002049551,0.0001575152,0.0002305573,0.0002961414,0.0004791262,0.0002500422,0.000209011,0.0005913165],"category_scores_gemma":[0.0003251019,0.0001013856,0.000354582,0.0002040968,0.000342892,0.000255292,0.0002710362,0.0001401943,0.0000311177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009649139,"about_ca_system_score_gemma":0.0003323995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07039946,"about_ca_topic_score_gemma":0.1123583,"domain_scores_codex":[0.9998972,0.00002641678,0.000005561792,0.00002598753,0.00001265159,0.00003208353],"domain_scores_gemma":[0.9998293,0.00003778182,0.00003403347,0.00001491409,0.00003019921,0.00005372732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005210832,0.0001922852,0.9204381,0.00003636993,0.0002850335,0.0003884905,0.0002050542,0.04056408,0.03050753,0.0003410416,0.0002865358,0.006234504],"study_design_scores_gemma":[0.00001188038,0.00005924322,0.9735706,0.000002503555,0.00002714107,0.00004746147,0.0001184823,0.02550753,0.0004493414,0.0000557708,0.0001417672,0.000008168499],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997097,0.00001029959,0.00004966509,0.000007020327,7.850192e-7,0.000001307078,0.00006609024,0.000004282744,0.0001508987],"genre_scores_gemma":[0.999822,0.000006457954,0.00007387609,0.000003180286,5.886376e-7,0.000001086201,0.00006160087,0.00000107687,0.00003013701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07039946,"threshold_uncertainty_score":0.1399794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235555558734308,"score_gpt":0.2618510534468056,"score_spread":0.2394954978594626,"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."}}