{"id":"W4396618406","doi":"10.5194/tc-18-2207-2024","title":"SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition","year":2024,"lang":"en","type":"article","venue":"The cryosphere","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft","keywords":"Mosaic; Remote sensing; Scanner; Geology; Sea ice; Artificial intelligence; Geography; Computer science; Oceanography; Archaeology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004572713,0.0002009782,0.0001428147,0.000009992008,0.0005295764,0.0001856398,0.0003061481,0.00008088576,0.004246788],"category_scores_gemma":[0.00003264447,0.0001027707,0.00007335796,0.0003257009,0.0001645909,0.0002493987,0.00001647945,0.0004722864,0.0004609542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001847565,"about_ca_system_score_gemma":0.00009023479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003711134,"about_ca_topic_score_gemma":0.0100869,"domain_scores_codex":[0.9983724,0.0001785758,0.0001839544,0.0003340651,0.0005794722,0.0003515571],"domain_scores_gemma":[0.9991845,0.0003633505,0.00006225804,0.0002424624,0.00005362375,0.00009382227],"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":[0.001272094,0.00009157472,0.6160717,0.0001393263,0.0009470828,0.0002565806,0.01469639,0.07481508,0.00009403026,0.0001350389,0.02321571,0.2682654],"study_design_scores_gemma":[0.001334091,0.0006096917,0.4229366,0.0004150033,0.000525869,0.00008523556,0.01287597,0.4229831,0.00007889549,0.002023037,0.1352147,0.000917751],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814855,0.002227409,0.004669589,0.00312872,0.001083517,0.0004380128,0.0002043955,0.000256327,0.006506558],"genre_scores_gemma":[0.9959704,0.00005615785,0.0003785797,0.0006753476,0.0006417144,0.000001445013,0.0004021699,0.00001482636,0.001859318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.348168,"threshold_uncertainty_score":0.9966635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133786313532845,"score_gpt":0.2010437025560168,"score_spread":0.1876650712027323,"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."}}