{"id":"W3070319597","doi":"10.1017/jog.2020.64","title":"Slope estimation influences on ice thickness inversion models: a case study for Monte Tronador glaciers, North Patagonian Andes","year":2020,"lang":"en","type":"article","venue":"Journal of Glaciology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Airbus Defense and Space; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Geology; Glacier; Smoothing; Inversion (geology); Geomorphology; Glacier morphology; Geodesy; Climatology; Ice stream; Sea ice; Cryosphere; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690743,0.0001196766,0.0002828866,0.00003902905,0.0002920258,0.00002598738,0.0001594066,0.00005041843,0.00005812745],"category_scores_gemma":[0.0002254478,0.00008424103,0.00007811408,0.0001839725,0.0000615945,0.0003176529,0.00001320959,0.0001739037,0.00000587362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009100718,"about_ca_system_score_gemma":0.00007181062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006167762,"about_ca_topic_score_gemma":0.004065029,"domain_scores_codex":[0.9990225,0.0001024231,0.0003768561,0.0001590152,0.0001614015,0.0001777612],"domain_scores_gemma":[0.9989789,0.0003650815,0.0003052538,0.00007546622,0.0001633999,0.0001119393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007897703,0.0000621292,0.6916251,0.00002984192,0.0001273758,0.000448848,0.01037811,0.2620915,0.000005340098,0.00001693203,0.00140878,0.03301638],"study_design_scores_gemma":[0.002093508,0.0066465,0.5064687,0.00002083086,0.0001507965,0.0004216078,0.03481776,0.4465181,0.000003330631,0.0003435207,0.002290464,0.0002248726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99411,0.0003767879,0.00298841,0.001919601,0.0002712567,0.0002532895,0.0000392915,0.00001031826,0.00003103759],"genre_scores_gemma":[0.9961205,0.0000842532,0.002316921,0.001287627,0.0001696601,0.000001900792,0.000009861255,0.000002843415,0.000006436449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1851564,"threshold_uncertainty_score":0.3435249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0661656661986892,"score_gpt":0.2677210855611138,"score_spread":0.2015554193624246,"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."}}