{"id":"W4322742244","doi":"10.5194/tc-17-977-2023","title":"Spatio-temporal reconstruction of winter glacier mass balance in the Alps, Scandinavia, Central Asia and western Canada (1981–2019) using climate reanalyses and machine learning","year":2023,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Glacier; Snow; Glacier mass balance; Climatology; Precipitation; Environmental science; Physical geography; Climate change; Meteorology; Geology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002777147,0.0001173117,0.0001756463,0.00001154631,0.0003001461,0.00005610508,0.00011089,0.00003200867,0.0001624125],"category_scores_gemma":[0.00001602167,0.0000739559,0.00002592444,0.0003522706,0.0001123904,0.0001124546,0.00002280463,0.0001480866,0.000002149618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001201231,"about_ca_system_score_gemma":0.00004997665,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5490525,"about_ca_topic_score_gemma":0.9410995,"domain_scores_codex":[0.9990266,0.0001025997,0.0002292222,0.0001827169,0.000178146,0.00028074],"domain_scores_gemma":[0.9995407,0.0001500546,0.0001238818,0.0001208169,0.00002362276,0.00004094952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002113769,0.000002019169,0.9901986,0.00001896346,0.00001906758,0.000007267534,0.0003324423,0.00363607,0.000006297191,0.000004032277,0.0001901789,0.005563984],"study_design_scores_gemma":[0.0001708237,0.0000275517,0.9259219,0.00003912591,0.00002483835,0.00002188172,0.002341866,0.06789635,0.000002113896,0.00004213436,0.003423239,0.00008818958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975377,0.00143417,0.00003806482,0.0004262219,0.0002051906,0.0001312133,0.00007698673,0.00001272584,0.0001377278],"genre_scores_gemma":[0.9988006,0.0005187743,0.0001852849,0.00008471553,0.00005008682,0.000001088391,0.00006989911,0.000004229353,0.000285306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.392047,"threshold_uncertainty_score":0.4539504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220778069690198,"score_gpt":0.2223973057893092,"score_spread":0.2001895250924072,"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."}}