{"id":"W4294287515","doi":"10.5194/tc-16-3531-2022","title":"Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing","year":2022,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Environment and Climate Change Canada","funders":"NASA Headquarters","keywords":"Snow; Cryosphere; Environmental science; Snowpack; Albedo (alchemy); Arctic; Glacier; Radar; Cloud cover; Land cover; Climate change; Climatology; Population; Earth observation; Physical geography; Satellite; Sea ice; Geology; Meteorology; Geography; Land use; Cloud computing; Oceanography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004399526,0.0001671651,0.0002751081,0.000004932833,0.0007781804,0.0000293881,0.0002944071,0.00002710414,0.002622311],"category_scores_gemma":[0.00002384489,0.000111773,0.0001163919,0.0004551484,0.00008296651,0.00009725855,0.0001139596,0.0001801125,0.00003611327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004857433,"about_ca_system_score_gemma":0.00005048401,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01766602,"about_ca_topic_score_gemma":0.001425524,"domain_scores_codex":[0.998345,0.0001491338,0.0004007624,0.0002590256,0.0004252816,0.0004208675],"domain_scores_gemma":[0.9992457,0.00008428062,0.0001360574,0.0003897563,0.00007238777,0.00007182752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006403632,0.00005007211,0.350322,0.000594985,0.0003163521,0.00008892296,0.000707833,0.02662271,0.001069456,0.0001140895,0.003696231,0.6163533],"study_design_scores_gemma":[0.002135557,0.000732635,0.797374,0.002928934,0.001414008,0.0005968489,0.01165927,0.06025946,0.001309421,0.01251833,0.1068581,0.002213474],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9470534,0.04811952,0.001214877,0.001056781,0.001418823,0.0003824863,0.00008708899,0.00005451732,0.000612475],"genre_scores_gemma":[0.9862416,0.001653172,0.01118258,0.0005101064,0.0002390338,5.494396e-7,0.00002454124,0.000008253322,0.0001401464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6141399,"threshold_uncertainty_score":0.9982894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492156749784312,"score_gpt":0.2533520571552955,"score_spread":0.2184304896574523,"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."}}