{"id":"W4220881868","doi":"10.5194/egusphere-egu22-10534","title":"Observationally calibrating snow-on-sea-ice model free parameters and estimating uncertainties using a Markov Chain Monte Carlo method","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Snow; Sea ice; Arctic; Sea ice thickness; Arctic ice pack; Environmental science; Ice-albedo feedback; Sea ice concentration; Climatology; Cryosphere; Atmospheric sciences; Snow field; Geology; Meteorology; Oceanography; Snow cover; 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":[0.003582234,0.0007518418,0.0009920903,0.0012268,0.0008701953,0.001435434,0.002044476,0.001249918,0.004048525],"category_scores_gemma":[0.01075406,0.001273589,0.001225608,0.0009388211,0.001278768,0.001505349,0.001287314,0.001773772,0.0007381323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00161307,"about_ca_system_score_gemma":0.002727328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04044811,"about_ca_topic_score_gemma":0.03466686,"domain_scores_codex":[0.999018,0.0004181928,0.00005135631,0.0002868465,0.0001306618,0.00009496734],"domain_scores_gemma":[0.9919854,0.006221322,0.0004596536,0.0004678825,0.0006978234,0.0001678173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003436674,0.00002238884,0.001888976,0.00002188794,0.00003735748,0.00003096411,0.00002669775,0.9843324,0.0002191541,0.004807089,0.000313458,0.008265268],"study_design_scores_gemma":[0.000005498712,0.000003683706,0.0001887583,0.000007977121,0.000003873952,0.000005004658,0.000003601487,0.9966351,0.0001170564,0.002875442,0.0001481595,0.00000575026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04581604,0.0001761209,0.950452,0.0002047931,0.00004291742,0.00008517537,0.0004404617,0.0007585005,0.002024042],"genre_scores_gemma":[0.7121688,0.0002210575,0.2818741,0.0001891169,0.00007958191,0.0003522061,0.002253858,0.000254093,0.002607315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04044811,"threshold_uncertainty_score":0.08042532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05198353879129478,"score_gpt":0.2743723650083573,"score_spread":0.2223888262170625,"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."}}