{"id":"W4408435841","doi":"10.5194/egusphere-egu25-7414","title":"Tracking improvements in remotely sensed snow water equivalent from GlobSnow to the ESA Snow CCI program","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Snow; Water equivalent; Tracking (education); Environmental science; Remote sensing; Meteorology; Geography","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":[0.003871768,0.0008209939,0.0003878455,0.001217681,0.0003846988,0.001044292,0.0008752563,0.0006496654,0.001219165],"category_scores_gemma":[0.006048219,0.0003694001,0.0004641577,0.002021747,0.0003178592,0.001295537,0.0009360513,0.0008977593,0.001002778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306207,"about_ca_system_score_gemma":0.000682393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02490171,"about_ca_topic_score_gemma":0.02369248,"domain_scores_codex":[0.9990446,0.0001051878,0.00003615071,0.0003054313,0.0004281471,0.00008041409],"domain_scores_gemma":[0.9973224,0.0002986805,0.0001736654,0.0004108091,0.001651086,0.0001433899],"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":[0.001598002,0.0006350345,0.2373227,0.0005730862,0.0005743557,0.0002778659,0.001111399,0.08868771,0.05553828,0.00587001,0.1013197,0.506492],"study_design_scores_gemma":[0.0004238132,0.0005920793,0.4028782,0.0001804859,0.0002543407,0.000150829,0.0003333746,0.280782,0.06938026,0.002566698,0.2423014,0.0001565267],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8275148,0.001671374,0.06285759,0.002309752,0.0008257129,0.0006082795,0.04284658,0.02496805,0.03639794],"genre_scores_gemma":[0.7029732,0.0006333428,0.1966836,0.0007587383,0.0001488319,0.0003596487,0.08857704,0.004178435,0.005687119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02490171,"threshold_uncertainty_score":0.04951352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04430505092008324,"score_gpt":0.2791410643468228,"score_spread":0.2348360134267396,"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."}}