{"id":"W4322005254","doi":"10.5194/egusphere-egu23-7408","title":"Landscape generation by subglacial hydrology beneath the Fennoscandian Ice Sheet","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":"University of Waterloo","funders":"","keywords":"Geology; Meltwater; Ice stream; Geomorphology; Landform; Ice sheet; Glacier; Digital elevation model; Sediment transport; Glacier morphology; Sediment; Hydrology (agriculture); Cryosphere; Oceanography; Sea ice; Remote sensing","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":[0.00007943125,0.0001962165,0.0001393918,0.0006152687,0.0002494831,0.0007359522,0.0003074712,0.0002032583,0.003356006],"category_scores_gemma":[0.0001702463,0.00009573235,0.0002592724,0.0004942071,0.0002003746,0.0001648792,0.0002443239,0.0001017781,0.0001347199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001209846,"about_ca_system_score_gemma":0.000631121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1277735,"about_ca_topic_score_gemma":0.1834086,"domain_scores_codex":[0.9999387,0.000006178797,0.000001984922,0.00002422082,0.000007777639,0.00002110223],"domain_scores_gemma":[0.9999545,0.000008117109,0.000007657361,0.000005178172,0.000009854052,0.00001467726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003916812,0.0001117485,0.7564232,0.00009056032,0.0002207178,0.0004813657,0.0005109258,0.1751238,0.01081947,0.001961345,0.002782537,0.05108261],"study_design_scores_gemma":[0.00001493485,0.00002203942,0.9118671,0.000009425921,0.00003618208,0.00007639917,0.0003577054,0.08558238,0.0002781728,0.0003184438,0.001424293,0.00001292119],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962288,0.00006424294,0.000245199,0.00002568033,0.000003222293,0.000006775523,0.001060309,0.00004086615,0.002324903],"genre_scores_gemma":[0.998618,0.00003835111,0.0002909017,0.00000695188,0.000001344602,0.000005835926,0.0007169377,0.000005402323,0.000316244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1277735,"threshold_uncertainty_score":0.2540596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04052138502223477,"score_gpt":0.2316943732359713,"score_spread":0.1911729882137365,"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."}}