{"id":"W2325045102","doi":"10.1007/s00382-016-3104-9","title":"Impact of lake–river connectivity and interflow on the Canadian RCM simulated regional climate and hydrology for Northeast Canada","year":2016,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Interflow; Streamflow; Environmental science; Hydrology (agriculture); Precipitation; Climate model; Routing (electronic design automation); Hydrometeorology; Climate change; Climatology; Surface water; Wetland; Drainage basin; Geology; Groundwater; Geography; Oceanography; Ecology; Meteorology","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":[0.0002097624,0.000120392,0.0001510915,0.00002754301,0.0003128482,0.000006196948,0.00009562925,0.00005436873,0.00006634775],"category_scores_gemma":[0.00003697411,0.00006727814,0.00002985053,0.00004152911,0.0005508057,0.00005298073,0.0001365767,0.00005476745,0.000004379532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002507572,"about_ca_system_score_gemma":0.00002426861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4761923,"about_ca_topic_score_gemma":0.9961737,"domain_scores_codex":[0.9992109,0.00004239935,0.0001148774,0.0002119202,0.00006409774,0.000355799],"domain_scores_gemma":[0.9994456,0.0002597794,0.00006181405,0.000141685,0.00000906694,0.00008205679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001829727,0.00001701292,0.9950547,0.00001135779,0.00006934162,0.000005553401,0.0001329669,0.001859119,0.00002960176,0.001103045,0.0005014135,0.00103288],"study_design_scores_gemma":[0.0005469448,0.0002475133,0.9191425,0.00001547556,0.0000300827,0.000005962881,0.00003034044,0.07829478,0.000003334911,0.0009596088,0.000588062,0.0001354281],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995025,0.000004855461,0.00002508071,0.003529817,0.00005135655,0.0002177222,0.0004258037,0.000007024669,0.000713388],"genre_scores_gemma":[0.9994124,0.00009194525,0.000009533544,0.0004142778,0.000005768794,0.000007590925,0.00001632155,0.000008020406,0.00003414818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5199814,"threshold_uncertainty_score":0.5272958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009166780499716518,"score_gpt":0.2176730349082185,"score_spread":0.208506254408502,"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."}}