{"id":"W4401620542","doi":"10.1002/ecy.4398","title":"Peat profile database from peatlands in Canada","year":2024,"lang":"en","type":"article","venue":"Ecology","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Government of New Brunswick; Ministry of Natural Resources and Forestry; Ontario Forest Research Institute; Université du Québec à Montréal; Canadian Forest Service; Environment and Climate Change Canada; University of Waterloo","funders":"Government of Canada","keywords":"Peat; Environmental science; Bulk density; Carbon fibers; Hydrology (agriculture); Table (database); Bog; Water table; Ombrotrophic; Greenhouse gas; Litter; Hectare; Carbon cycle; Soil science; Physical geography; Ecosystem; Ecology; Geology; Database; Geography; Soil water; Groundwater; Mathematics; Archaeology","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":[0.0003168825,0.000772437,0.000613898,0.006756078,0.001276172,0.001236317,0.001271273,0.0004471982,0.01270884],"category_scores_gemma":[0.001815871,0.0003226333,0.0005070901,0.01262474,0.0002305116,0.0006209642,0.0008169502,0.0004351873,0.007006847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007230087,"about_ca_system_score_gemma":0.01740378,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9440107,"about_ca_topic_score_gemma":0.9755841,"domain_scores_codex":[0.9995463,0.00001697328,0.00005583723,0.0000883633,0.0001745803,0.0001178014],"domain_scores_gemma":[0.9979557,0.00008723568,0.0001387015,0.0001263318,0.001490562,0.0002014484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004043128,0.0001156229,0.06278679,0.002850588,0.0001940477,0.0006894818,0.0005960706,0.003870903,0.001929949,0.003000932,0.8519176,0.07164374],"study_design_scores_gemma":[0.00009183859,0.00002481647,0.1463471,0.0009116071,0.0001157707,0.0002646984,0.0008414385,0.003345098,0.001360537,0.0007893562,0.8458149,0.00009271801],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007329332,0.0004885001,0.0003258037,0.0000301641,0.000009936162,0.00007112695,0.9867885,0.0002243097,0.004732209],"genre_scores_gemma":[0.01266845,0.0005069224,0.001375063,0.00003645312,0.000003159703,0.0001230076,0.9832812,0.0000523593,0.001953299],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05598933,"threshold_uncertainty_score":0.1126381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006758101065899236,"score_gpt":0.2061644762380784,"score_spread":0.1994063751721791,"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."}}