{"id":"W2979543687","doi":"10.4095/314909","title":"Long-term hydrological dynamics of Canada's largest watershed: climate controls on water quantity of the Mackenzie River Basin","year":2019,"lang":"en","type":"report","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Watershed; Term (time); Structural basin; Drainage basin; Hydrology (agriculture); Environmental science; Geography; Physical geography; Geology; Cartography; Geomorphology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002375492,0.0001263763,0.0001829157,0.0006162191,0.001427216,0.001247647,0.0005608721,0.00027514,0.001904932],"category_scores_gemma":[0.0009269738,0.0001333372,0.0002099011,0.001136285,0.0008411381,0.0004490553,0.0007415724,0.0003979273,0.0001547704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01481074,"about_ca_system_score_gemma":0.019155,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.988782,"about_ca_topic_score_gemma":0.9960406,"domain_scores_codex":[0.9998611,0.000009703964,0.000005939851,0.00002229915,0.00004415969,0.00005681144],"domain_scores_gemma":[0.9989601,0.00006689312,0.0001216192,0.00002540154,0.000493962,0.0003320265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001040653,0.00008025851,0.9814761,0.00003476846,0.00009036263,0.0001774862,0.0008378504,0.001493688,0.001532146,0.001454468,0.004667458,0.008051323],"study_design_scores_gemma":[0.000005725632,0.000005575891,0.9954737,0.00001495734,0.00001756398,0.00002150712,0.0009655667,0.001277605,0.0001156455,0.000118672,0.001972681,0.00001071551],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917873,0.0002788776,0.00006827812,0.001292747,0.000008168015,0.00001084878,0.003231355,0.00001143581,0.003311053],"genre_scores_gemma":[0.996867,0.0002449484,0.00009311966,0.00008086959,0.000004971435,0.00000558048,0.001287389,0.000005507918,0.001410613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01481074,"threshold_uncertainty_score":0.10746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430445142138745,"score_gpt":0.2307456564164047,"score_spread":0.2164412049950173,"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."}}