{"id":"W2023588280","doi":"10.5558/tfc81142-1","title":"The role of forests in regulating water: The Turkey Lakes Watershed case study","year":2005,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; Natural Resources Canada; U.S. Forest Service","keywords":"Watershed; Environmental science; Water quality; Hydrology (agriculture); Surface runoff; Drainage basin; STREAMS; Disturbance (geology); Geography; Ecology; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003183778,0.0002124528,0.0001572997,0.000443224,0.001378059,0.0007766012,0.0006662012,0.0007734137,0.001323683],"category_scores_gemma":[0.000503914,0.0001033052,0.0001955007,0.00096949,0.0009692546,0.0006213146,0.0007256604,0.0003482143,0.00008740726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003627958,"about_ca_system_score_gemma":0.002191677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09415092,"about_ca_topic_score_gemma":0.1892145,"domain_scores_codex":[0.9997568,0.00007195073,0.0000104643,0.00002652382,0.00002990681,0.0001043251],"domain_scores_gemma":[0.9998099,0.00005635134,0.00003599243,0.00001273321,0.00003577563,0.00004924435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001109045,0.002800593,0.6057104,0.0003854448,0.0001896141,0.08690827,0.0116929,0.1021408,0.01097623,0.03465421,0.01516109,0.1282715],"study_design_scores_gemma":[0.0004783539,0.001382652,0.6748295,0.0002464911,0.0002712212,0.009559199,0.07637913,0.1556481,0.009777666,0.01212389,0.05909752,0.0002061674],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928272,0.0001021257,0.0004515032,0.0002754244,0.000004853735,0.00005599783,0.0001332357,0.00001230308,0.006137248],"genre_scores_gemma":[0.9977024,0.0001355006,0.0007663522,0.00003933047,0.000003945684,0.00003003128,0.00009063258,0.000003948598,0.001227853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09415092,"threshold_uncertainty_score":0.1872058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008194783553810675,"score_gpt":0.2267945155796325,"score_spread":0.2185997320258218,"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."}}