{"id":"W4380997511","doi":"10.5194/tc-17-2367-2023","title":"Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar","year":2023,"lang":"en","type":"article","venue":"The cryosphere","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Global Water Futures; Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada; Environment and Natural Resources, Northwest Territories; Polar Knowledge Canada","keywords":"Snow; Snowpack; Ground-penetrating radar; Transect; Geology; Arctic; Snow field; Physical geography; Environmental science; Hydrology (agriculture); Radar; Geomorphology; Oceanography; Snow cover; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00044515,0.0002193204,0.0001796705,0.00006539861,0.001041499,0.0001590246,0.0003645261,0.00009894037,0.0009422022],"category_scores_gemma":[0.00004541109,0.00016621,0.00008642719,0.0006722859,0.0001206011,0.0001785174,0.00001829315,0.0003378156,0.0009020112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004975415,"about_ca_system_score_gemma":0.0002856874,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3302848,"about_ca_topic_score_gemma":0.8397033,"domain_scores_codex":[0.998232,0.0001013634,0.0002441363,0.0003259947,0.0003367488,0.0007597342],"domain_scores_gemma":[0.9989622,0.0003546151,0.000089867,0.0003000008,0.00003840458,0.0002549252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002935613,0.00001556565,0.8933648,0.0001139131,0.0001097272,0.0003027312,0.002761287,0.01253171,0.0001889252,0.000415384,0.001260214,0.08890633],"study_design_scores_gemma":[0.0002415676,0.00008493127,0.8964643,0.0001992106,0.00002944154,0.00007979444,0.006049708,0.08838607,0.00000502896,0.001483519,0.006544021,0.000432437],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989648,0.0001018808,0.00012898,0.0005458159,0.0006552584,0.0002098593,0.00004188831,0.0001068896,0.008561458],"genre_scores_gemma":[0.9971303,0.00004408674,0.0004209759,0.001052635,0.0003023968,0.000001290837,0.0001402844,0.00001509883,0.0008929751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5094185,"threshold_uncertainty_score":0.9999711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204039556725986,"score_gpt":0.2158209775749866,"score_spread":0.1937805820077267,"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."}}