{"id":"W2591291746","doi":"10.1016/j.rse.2017.02.006","title":"Application of a Markov Chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements","year":2017,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Space Agency; European Space Agency; China Scholarship Council; National Aeronautics and Space Administration","keywords":"Snow; Outlier; Markov chain Monte Carlo; Remote sensing; Algorithm; Microwave; Brightness temperature; Computer science; Monte Carlo method; Environmental science; Bayesian probability; Meteorology; Artificial intelligence; Mathematics; Statistics; Geology; Physics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002752385,0.0005836936,0.001201875,0.0009008174,0.001266993,0.001104757,0.001740286,0.00151309,0.003529405],"category_scores_gemma":[0.008373166,0.00104426,0.0009109691,0.001060479,0.0009023959,0.001335747,0.001283997,0.001627357,0.0006627912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111053,"about_ca_system_score_gemma":0.003454459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02846479,"about_ca_topic_score_gemma":0.02698093,"domain_scores_codex":[0.9991905,0.0003625517,0.00005547305,0.0001372134,0.0001799445,0.00007418698],"domain_scores_gemma":[0.9924577,0.006114059,0.00023298,0.0002955678,0.0007441902,0.0001555603],"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":[0.0001337256,0.00006575197,0.0008389959,0.00003347953,0.00005260599,0.00004443141,0.00004681213,0.9609312,0.0007493615,0.00888153,0.0005407122,0.0276813],"study_design_scores_gemma":[0.000008741455,0.000004398079,0.00003625199,0.000001950348,0.000002693836,0.000004881508,0.000001594222,0.9984205,0.0001295151,0.001312724,0.00007340703,0.000003376195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01586975,0.0001010112,0.9823741,0.0001328472,0.00003842756,0.00005787003,0.00005827938,0.0004908657,0.000876835],"genre_scores_gemma":[0.3460551,0.0002104686,0.6496802,0.0001674391,0.00009177217,0.0003618469,0.0005320631,0.0002600096,0.002641088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02846479,"threshold_uncertainty_score":0.05659825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03192171417351844,"score_gpt":0.2267955331030525,"score_spread":0.1948738189295341,"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."}}