{"id":"W3176041963","doi":"10.1038/s41597-021-00939-2","title":"GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset","year":2021,"lang":"en","type":"article","venue":"Scientific Data","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":221,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Academy of Finland; European Space Agency","keywords":"Snow; Water equivalent; Northern Hemisphere; Environmental science; Snowpack; Radiometer; Climatology; Defense Meteorological Satellite Program; Satellite; Meteorology; Remote sensing; Geography; Geology","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":["insufficient_payload"],"category_scores_codex":[0.0005487386,0.0001397656,0.0001506746,0.00001256056,0.0006986864,0.0005108349,0.001184259,0.00003671434,0.01717996],"category_scores_gemma":[0.0001538754,0.00009824557,0.00003588375,0.0003998861,0.0001970527,0.000482262,0.0005580274,0.00009787664,0.003723868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006053468,"about_ca_system_score_gemma":0.0001110688,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001005555,"about_ca_topic_score_gemma":0.05046273,"domain_scores_codex":[0.9979297,0.00004210648,0.0002360844,0.0008684818,0.0004543279,0.0004693275],"domain_scores_gemma":[0.9974014,0.00006979117,0.00004121444,0.002236337,0.0001055792,0.0001457209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006152465,0.00004223862,0.07355642,0.00001989542,0.00003751405,0.00006195655,0.0001271664,0.0002551177,0.0001574003,0.00001349586,0.8822046,0.04351803],"study_design_scores_gemma":[0.000139862,0.000008923392,0.0369873,0.00001351084,0.00002505495,0.00001174435,0.000491194,0.002479079,0.0003906121,0.0001712052,0.9591076,0.0001738722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.4335415,0.009027042,0.001182764,0.01059852,0.01562415,0.000626448,0.5168164,0.0004315611,0.0121516],"genre_scores_gemma":[0.1419806,0.0002027588,0.003983026,0.00109772,0.0005063557,0.000002604179,0.8356646,0.00001768638,0.0165447],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3188482,"threshold_uncertainty_score":0.9970518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07065568232724688,"score_gpt":0.2633220157493701,"score_spread":0.1926663334221232,"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."}}