{"id":"W2048903028","doi":"10.1016/j.rse.2008.09.010","title":"Seasonal snow extent and snow mass in South America using SMMR and SSM/I passive microwave data (1979–2006)","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Oceanic and Atmospheric Administration","keywords":"Snow; Environmental science; Snow cover; Snow line; Satellite; Climatology; Snow field; Special sensor microwave/imager; Radiometer; Snowpack; Physical geography; Meteorology; Remote sensing; Microwave; Geography; Geology; Brightness temperature","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003056865,0.000256026,0.0001911517,0.0007604608,0.0003372919,0.0003436196,0.0002867822,0.0002587519,0.0008567974],"category_scores_gemma":[0.0004889405,0.0001700758,0.000292795,0.001073809,0.000158154,0.0004295871,0.0002793022,0.0002264845,0.000180907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007030928,"about_ca_system_score_gemma":0.0005225624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2129762,"about_ca_topic_score_gemma":0.4589373,"domain_scores_codex":[0.99993,0.00001011186,0.000008811563,0.00002417636,0.00001284885,0.00001401265],"domain_scores_gemma":[0.9995626,0.00005556456,0.0001565654,0.00002585146,0.0001288388,0.00007059739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001525766,0.00003418329,0.9915537,0.00004896427,0.0001682098,0.00009491691,0.0003551054,0.0005792164,0.002778382,0.00003326804,0.0008615436,0.003339956],"study_design_scores_gemma":[0.000002410978,0.000004462978,0.9991539,0.000003497851,0.00001952188,0.00002027503,0.00008824903,0.0001960373,0.00009385858,0.000003198999,0.0004130498,0.000001617589],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963734,0.000182664,0.00004991446,0.00003848474,0.000004658291,0.000004419834,0.002732619,0.000008641525,0.0006052073],"genre_scores_gemma":[0.9947313,0.0001897682,0.0002547367,0.00002649404,0.000009271095,0.00001107067,0.004253974,0.000005856085,0.0005175938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2129762,"threshold_uncertainty_score":0.4234732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04298521174819735,"score_gpt":0.2170650168047599,"score_spread":0.1740798050565625,"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."}}