{"id":"W4391870763","doi":"10.1016/j.ecolmodel.2024.110633","title":"Using the Canadian Model for Peatlands (CaMP) to examine greenhouse gas emissions and carbon sink strength in Canada's boreal and temperate peatlands","year":2024,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; Environment and Climate Change Canada","funders":"Canadian Forest Service","keywords":"Peat; Boreal; Temperate climate; Environmental science; Greenhouse gas; Carbon sink; Sink (geography); Atmospheric sciences; Carbon fibers; Ecology; Climate change; Geography; Biology; Geology; Mathematics","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":[0.0003127615,0.0002026983,0.0002396519,0.00007401747,0.0003525923,0.00009583568,0.0001454623,0.0001269527,0.00003384822],"category_scores_gemma":[0.00003113322,0.0001319644,0.00002620976,0.0001816615,0.00007341285,0.00005894353,0.0001257052,0.0002377499,4.5673e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00070679,"about_ca_system_score_gemma":0.0003136974,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9083263,"about_ca_topic_score_gemma":0.9976296,"domain_scores_codex":[0.9985026,0.0000450807,0.0002427143,0.0004919341,0.000132207,0.0005854307],"domain_scores_gemma":[0.9992219,0.0002072973,0.00002349789,0.0001373664,0.000007908554,0.0004020498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000368963,0.00002921016,0.1972159,0.00001679426,0.00001472455,0.00009002189,0.0007384687,0.7991773,0.0002379523,0.0001584846,0.0004141508,0.00187004],"study_design_scores_gemma":[0.0002428691,0.00009801951,0.01949944,0.00002014639,0.0000205001,0.00002041433,0.00006429725,0.9783659,0.000009215472,0.0007821893,0.0006803583,0.0001966432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947503,0.0001077694,0.002372797,0.001344641,0.00006510549,0.0003670361,0.000048379,0.00002196917,0.0009219892],"genre_scores_gemma":[0.9977187,0.00009561815,0.001548193,0.00031249,0.00005728846,0.00004887039,0.00004057991,0.00001831938,0.0001599356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1791886,"threshold_uncertainty_score":0.5381353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03620760756437768,"score_gpt":0.2439875534168768,"score_spread":0.2077799458524991,"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."}}