{"id":"W2309351159","doi":"10.5558/tfc2016-010","title":"Marmot Creek Experimental Watershed Study","year":2016,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of Saskatchewan; University of Alberta","funders":"","keywords":"Marmot; Hydrograph; Streamflow; Watershed; Clearing; Climate change; Environmental science; Hydrology (agriculture); Snowmelt; Snow; Precipitation; Structural basin; Drainage basin; Physical geography; Geography; Ecology; Geology; Meteorology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001468704,0.0005437603,0.0008858593,0.0006055182,0.003944953,0.001010025,0.001146064,0.0008036949,0.009030677],"category_scores_gemma":[0.0009871569,0.0002874457,0.0003269197,0.001174662,0.001283562,0.0007965568,0.001312508,0.001312765,0.001006016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002372246,"about_ca_system_score_gemma":0.00423959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03067867,"about_ca_topic_score_gemma":0.07332461,"domain_scores_codex":[0.9984706,0.0004422823,0.00008918287,0.000342934,0.0004301719,0.0002248548],"domain_scores_gemma":[0.9992046,0.00005677923,0.00008305916,0.0001082923,0.0001636294,0.0003836396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03108114,0.06518845,0.3438193,0.002114735,0.001399166,0.02190467,0.008461261,0.002458801,0.07681502,0.05214565,0.1008579,0.2937539],"study_design_scores_gemma":[0.003038748,0.03757013,0.6979697,0.0004593688,0.0006749114,0.003588195,0.006539741,0.002048925,0.01088897,0.008971089,0.227982,0.0002683075],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565238,0.001713777,0.002386017,0.001327249,0.0003212886,0.005587607,0.00467244,0.0001144336,0.02735326],"genre_scores_gemma":[0.9472797,0.00321153,0.008729856,0.002166353,0.0002252482,0.01068113,0.007464123,0.00006384102,0.02017823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03067867,"threshold_uncertainty_score":0.06100017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01459596019196173,"score_gpt":0.2401232858704391,"score_spread":0.2255273256784774,"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."}}