{"id":"W2171377191","doi":"10.1002/hyp.6376","title":"The effects of water table draw‐down (as a surrogate for climate change) on the hydrology of a fen peatland, Canada","year":2006,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":170,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Peat; Water table; Lawn; Hydrology (agriculture); Environmental science; Ombrotrophic; Hydraulic conductivity; Wetland; Mire; Ridge; Biogeochemical cycle; Sphagnum; Water level; Bog; Climate change; Geology; Soil science; Atmospheric sciences; Soil water; Groundwater; Ecology; Geography; Geotechnical engineering","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":[0.0003438652,0.0001582591,0.0002720802,0.00001279153,0.0002996011,0.00001219528,0.0003680591,0.00009850784,0.0001599747],"category_scores_gemma":[0.0002388599,0.00006326198,0.00004890056,0.0001233594,0.0003823708,0.00004201827,0.0001526065,0.0001004672,0.00001212747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003187421,"about_ca_system_score_gemma":0.00002947386,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06903298,"about_ca_topic_score_gemma":0.1987326,"domain_scores_codex":[0.9986563,0.00008511414,0.0002590139,0.0002664213,0.0001910812,0.0005420254],"domain_scores_gemma":[0.9983,0.001304678,0.0001304095,0.0001992985,0.00002291458,0.00004271202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004810104,0.001895232,0.8783966,0.002124579,0.0002388473,0.0002009442,0.001414199,0.005105244,0.05844847,0.006795189,0.03623448,0.004336143],"study_design_scores_gemma":[0.007608703,0.01487633,0.2565467,0.0002079835,0.0003848004,0.0001406851,0.0001870042,0.004544413,0.3038616,0.09342912,0.3166594,0.001553249],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919165,0.0002104802,0.000007982166,0.003147262,0.00008131721,0.000570609,0.00001477522,0.00001385382,0.004037256],"genre_scores_gemma":[0.9983642,0.0001741185,0.00000645305,0.0008462217,0.00005814463,0.0003040469,0.00001627959,0.000008466213,0.0002221366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6218498,"threshold_uncertainty_score":0.9371664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007018424436646152,"score_gpt":0.1979059802903219,"score_spread":0.1908875558536758,"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."}}