{"id":"W2053853125","doi":"10.1016/j.jhydrol.2006.10.019","title":"Impacts of dams on monthly flow characteristics. The influence of watershed size and seasons","year":2006,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Watershed; Skewness; Hydrology (agriculture); Magnitude (astronomy); STREAMS; Flow (mathematics); Kurtosis; Geology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003086551,0.00006627676,0.0002069352,0.00002412672,0.00005452255,0.000002533901,0.0001406281,0.00004113533,0.000114142],"category_scores_gemma":[0.0001255485,0.0000399363,0.00003550723,0.00004790897,0.0003872724,0.00007545886,0.00009887036,0.0001045389,0.000005770281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001655695,"about_ca_system_score_gemma":0.000004033979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007499718,"about_ca_topic_score_gemma":0.000226778,"domain_scores_codex":[0.9994019,0.00005849464,0.0002363546,0.00006783753,0.0001043208,0.000131096],"domain_scores_gemma":[0.9993697,0.0001771769,0.0003282142,0.0000895104,0.00001234501,0.00002300413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002360864,0.0001436641,0.9659523,0.00001534511,0.00007914585,0.00003419792,0.0002996337,0.01364663,0.01081837,0.00007730031,0.008514246,0.0001830273],"study_design_scores_gemma":[0.0003147018,0.0004820631,0.9968218,0.000007157932,0.00003991312,0.0000139125,0.00001772516,0.0001865485,0.0005022843,0.00063159,0.000948082,0.00003428549],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952058,0.00002052809,0.000003379538,0.003378776,0.00005495641,0.00005603081,0.000004017757,0.000001645732,0.001274866],"genre_scores_gemma":[0.9992401,0.00006169592,0.00008888732,0.0005348301,0.00002179758,9.19185e-7,3.654931e-7,0.000002826562,0.00004857657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03086938,"threshold_uncertainty_score":0.1628555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003327975651966808,"score_gpt":0.1978420125042437,"score_spread":0.1945140368522769,"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."}}