{"id":"W4404341212","doi":"10.5194/tc-18-5139-2024","title":"Characterization of non-Gaussianity in the snow distributions of various landscapes","year":2024,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Polar Programs; HORIZON EUROPE Framework Programme; National Science Foundation; Bureau of Reclamation; Directorate for Geosciences; European Commission; Battelle","keywords":"Non-Gaussianity; Snow; Characterization (materials science); Statistical physics; Environmental science; Climatology; Meteorology; Physics; Geology; Cosmic microwave background; Optics","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":[0.0008160783,0.0001364471,0.0001642046,0.0009519876,0.0002546563,0.0004677695,0.0001458186,0.0001342033,0.0004225889],"category_scores_gemma":[0.001992742,0.00007885156,0.0002217337,0.0008594565,0.0002838799,0.0004683223,0.0002841362,0.000155808,0.00006727804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003117352,"about_ca_system_score_gemma":0.0002030903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006704876,"about_ca_topic_score_gemma":0.01538631,"domain_scores_codex":[0.9998052,0.0000448024,0.00001663791,0.00006299554,0.0000405163,0.00002984463],"domain_scores_gemma":[0.9987956,0.0005400585,0.0002168967,0.0001257244,0.0002447445,0.00007682125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001277349,0.00005128281,0.9351876,0.00004798907,0.0002038282,0.0002049129,0.000448988,0.02749661,0.008798261,0.001495647,0.0004890571,0.0254481],"study_design_scores_gemma":[0.00000516917,0.00003466839,0.9219716,0.000008828117,0.00002889126,0.0001498204,0.000399739,0.07388442,0.0009231556,0.001861624,0.0007192146,0.00001290317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901983,0.00007857278,0.00877085,0.00002281547,0.000002382492,0.00000819418,0.0002914276,0.00004003151,0.0005875822],"genre_scores_gemma":[0.9987311,0.00002159536,0.0008304438,0.000005396751,0.000001945799,0.000003075964,0.0003283713,0.000004807716,0.00007328833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006704876,"threshold_uncertainty_score":0.01333165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107893490173359,"score_gpt":0.213784795538797,"score_spread":0.2029954465214611,"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."}}