{"id":"W4321481334","doi":"10.5194/egusphere-egu23-5656","title":"Towards pan-Arctic glacier calving front variability with deep learning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glacier; Arctic; Physical geography; Geology; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Climatology; Satellite; Glacier mass balance; Ice calving; Climate change; Oceanography; Remote sensing; Geography; Digital elevation model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006453693,0.001143403,0.000473859,0.001542458,0.000426289,0.0007692967,0.001132748,0.001042661,0.0009528269],"category_scores_gemma":[0.001281156,0.0003338034,0.001013295,0.00122851,0.0003831486,0.0007491466,0.0008041208,0.001812185,0.0005796161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009059151,"about_ca_system_score_gemma":0.001011512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04047947,"about_ca_topic_score_gemma":0.05193432,"domain_scores_codex":[0.9997435,0.00003152746,0.00001106343,0.000111854,0.00004219805,0.00005986027],"domain_scores_gemma":[0.9996691,0.00008718801,0.00004491171,0.00005766915,0.0001076952,0.00003338994],"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":[0.0002766002,0.0005245206,0.06875234,0.0001204018,0.0002920179,0.0002137833,0.000157062,0.6597168,0.009259567,0.001402374,0.02678534,0.2324992],"study_design_scores_gemma":[0.00001483602,0.00001351689,0.005773955,0.00001420778,0.0000120709,0.00001594553,0.00002481684,0.990493,0.001261458,0.001114277,0.001252267,0.000009771161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7660624,0.002660252,0.19294,0.001538258,0.0004137316,0.000097501,0.01923967,0.01194647,0.005101747],"genre_scores_gemma":[0.8908916,0.0003631692,0.06476936,0.0003395297,0.0001580129,0.00007062192,0.04039218,0.0002912608,0.002724234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04047947,"threshold_uncertainty_score":0.08048773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0307772728849166,"score_gpt":0.2331646555220188,"score_spread":0.2023873826371022,"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."}}