{"id":"W2049602511","doi":"10.1016/j.jhydrol.2006.02.011","title":"Partitioning impacts of climate and regulation on water level variability in Great Slave Lake","year":2006,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Water balance; Forcing (mathematics); Environmental science; Water level; Hydrology (agriculture); Climate change; Magnitude (astronomy); Structural basin; Water year; Drainage basin; Climatology; Physical geography; Geology; Oceanography; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009953331,0.00006581866,0.0001771562,0.00006572907,0.0000502379,0.000003245427,0.00004780451,0.00005542443,0.0002960371],"category_scores_gemma":[0.00002682868,0.00004412113,0.00002796928,0.00004050203,0.000165437,0.0001246802,0.00006809967,0.00009545202,0.0000125274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002711092,"about_ca_system_score_gemma":0.000001562347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006916225,"about_ca_topic_score_gemma":0.0007149483,"domain_scores_codex":[0.9991918,0.0001343149,0.0003089088,0.00009726586,0.00008693683,0.0001807652],"domain_scores_gemma":[0.9997097,0.00006276086,0.0001353175,0.00006607716,0.000006157911,0.00002000738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001991128,0.00008484622,0.9736727,0.00001035874,0.00001601768,0.00002410079,0.0002383454,0.01987081,0.00503147,0.0002970402,0.0001903203,0.0003648225],"study_design_scores_gemma":[0.0006210271,0.0003038751,0.9778702,0.00001050449,0.00002249662,0.00003057637,0.000006099503,0.0008103381,0.003627349,0.0159888,0.0006554493,0.00005328037],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948192,0.000007040082,0.0001219769,0.001154483,0.00005742571,0.00004888834,0.00000173967,0.000002409186,0.003786875],"genre_scores_gemma":[0.9996375,0.00002918496,0.0001348104,0.0001245326,0.00002393443,0.00000106942,0.000002009099,0.000002823991,0.00004411407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01906047,"threshold_uncertainty_score":0.3241399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021535824962272,"score_gpt":0.2219167549691475,"score_spread":0.2117013967195247,"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."}}