{"id":"W2132196051","doi":"10.5194/tc-6-1463-2012","title":"Calibration of a surface mass balance model for global-scale applications","year":2012,"lang":"en","type":"article","venue":"The cryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; RWTH Aachen University; Norsk Polarinstitutt; Háskóli Íslands; Universiteit Utrecht; University of Alberta","keywords":"Glacier; Precipitation; Snow; Environmental science; Climatology; Energy balance; Glacier mass balance; Atmospheric sciences; Calibration; Meteorology; Automatic weather station; Climate model; Climate change; Geology; Geography; Physical geography; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001490565,0.0001005878,0.0001341246,0.000001628047,0.0002474335,0.00001983164,0.0001927125,0.00004946149,0.0002695524],"category_scores_gemma":[0.000005975357,0.00007245289,0.00006969544,0.0002409943,0.000069769,0.0001628842,0.00001065594,0.00004503676,0.0000201323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000757387,"about_ca_system_score_gemma":0.00003470113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006798718,"about_ca_topic_score_gemma":0.004718595,"domain_scores_codex":[0.9992393,0.00001795523,0.0001988671,0.0001349588,0.0001447781,0.0002641115],"domain_scores_gemma":[0.9994169,0.0001120541,0.0001009511,0.0002390006,0.00006129062,0.0000698041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001913691,0.00003065677,0.8487459,0.0000322217,0.00002540156,2.838052e-8,0.000299334,0.1373788,0.0000456624,0.001769886,0.006228766,0.005424178],"study_design_scores_gemma":[0.0001572521,0.00001940161,0.1692364,0.000004685142,0.00002994848,7.465364e-7,0.0003343796,0.8040755,0.00001449842,0.002909139,0.0231065,0.0001114838],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1315621,0.005460921,0.8554807,0.0007735369,0.0002958844,0.001086747,0.0007588168,0.00007289377,0.004508418],"genre_scores_gemma":[0.9771864,0.00007052402,0.02088249,0.0002214508,0.0001301512,0.00002563575,0.00008002641,0.000004011339,0.001399287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8456243,"threshold_uncertainty_score":0.2954543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143161717370114,"score_gpt":0.2335281937695974,"score_spread":0.2120965765958963,"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."}}