{"id":"W2072066638","doi":"10.1002/hyp.6999","title":"Using satellite imagery to validate snow distribution simulated by a hydrological model in large northern basins","year":2008,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; York University","funders":"","keywords":"Snowmelt; Snow; Moderate-resolution imaging spectroradiometer; Environmental science; Arctic; Climatology; Satellite imagery; Structural basin; Terrain; Climate change; Hydrological modelling; Satellite; Drainage basin; Meteorology; Geology; Remote sensing; Geography; Oceanography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021262,0.0002287707,0.0003252366,0.00002700477,0.0004406598,0.00003691193,0.000216561,0.0001528228,0.0003399942],"category_scores_gemma":[0.0005351395,0.0001672463,0.00005081292,0.0008612153,0.0001315231,0.0001692032,0.00004735727,0.000206548,0.00008781478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001785226,"about_ca_system_score_gemma":0.00005658864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005123208,"about_ca_topic_score_gemma":0.004303623,"domain_scores_codex":[0.9982429,0.00006539847,0.0003296716,0.0004915118,0.0002569274,0.0006135682],"domain_scores_gemma":[0.9992423,0.000259661,0.00008000332,0.0001499824,0.00009886343,0.0001691641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009386134,0.0001150839,0.6298222,0.00001251187,0.000007767218,0.00005101633,0.0001083326,0.3688415,0.00008685745,0.00000413896,0.0001658232,0.0006908872],"study_design_scores_gemma":[0.0004617471,0.0002616968,0.2844915,0.00001866797,0.00001770198,0.00002055032,0.00003720926,0.7048402,0.0001205139,0.0008089543,0.00852079,0.0004004173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895902,0.001016196,0.007543252,0.0006266659,0.00003683212,0.0002541042,0.0005239049,0.0001061123,0.0003027084],"genre_scores_gemma":[0.9967896,0.000497302,0.0006590615,0.001389951,0.00004194675,0.000003683247,0.0005563603,0.000005145503,0.00005697106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3453307,"threshold_uncertainty_score":0.6820105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06017746235694315,"score_gpt":0.2594289172223698,"score_spread":0.1992514548654267,"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."}}