{"id":"W3089112793","doi":"10.1002/hyp.1271/abstract","title":"Simulating Pan-Arctic Runoff With a Macro-Scale Terrestrial Water Balance Model","year":2002,"lang":"en","type":"article","venue":"University of New Hampshire Scholars Repository (University of New Hampshire at Manchester)","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Surface runoff; Arctic; Water balance; Precipitation; Latitude; Water cycle; Climatology; Vegetation (pathology); Drainage basin; Water content; Arctic vegetation; Hydrology (agriculture); Atmospheric sciences; Geology; Meteorology; Geography; Oceanography; Tundra","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001768371,0.000410038,0.0006435502,0.0002031335,0.0009287205,0.00007655488,0.0008860574,0.000306047,0.00323171],"category_scores_gemma":[0.00001273691,0.0004313419,0.0003047281,0.0003302872,0.0004015711,0.001533493,0.0002098754,0.0004390496,0.0002245683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013223,"about_ca_system_score_gemma":0.0001228739,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01168325,"about_ca_topic_score_gemma":0.01535352,"domain_scores_codex":[0.997501,0.0001285835,0.0002944343,0.0007571952,0.0006608769,0.0006579424],"domain_scores_gemma":[0.9981343,0.00013244,0.000388979,0.0006864861,0.0001153834,0.0005423756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002963508,0.0002190683,0.8962467,0.0003146922,0.0003320813,0.001008595,0.04637323,0.01298691,0.02151013,0.000004890856,0.01123601,0.006804137],"study_design_scores_gemma":[0.01989066,0.002021244,0.6312938,0.001652326,0.001103642,0.0007048446,0.02674103,0.2404656,0.002817471,0.0002026428,0.06971492,0.003391787],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931973,0.0002803974,0.000400307,0.0006070625,0.0002001079,0.0003363753,0.0002068305,0.00009353651,0.00467808],"genre_scores_gemma":[0.9742102,0.0001659242,0.002867758,0.00009016793,0.0001434669,1.855636e-8,0.0003099223,0.00001790936,0.02219462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.264953,"threshold_uncertainty_score":0.9998139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0318998142229258,"score_gpt":0.188433761659747,"score_spread":0.1565339474368212,"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."}}