{"id":"W4297231576","doi":"10.5194/tc-16-3843-2022","title":"Drone-based ground-penetrating radar (GPR) application to snow hydrology","year":2022,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université du Québec en Abitibi-Témiscamingue; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; École de technologie supérieure","keywords":"Snowpack; Snow; Ground-penetrating radar; Snowmelt; Environmental science; Meltwater; Hydrology (agriculture); Watershed; Geology; Radar; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001877912,0.0001939851,0.0001282483,0.0005474271,0.00009797853,0.0003328028,0.0002119583,0.0001825498,0.00075515],"category_scores_gemma":[0.0003045825,0.00009128642,0.0001247945,0.0005651208,0.00008150891,0.0001835212,0.0001917307,0.0001224222,0.0001784514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003002881,"about_ca_system_score_gemma":0.0002770033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02810352,"about_ca_topic_score_gemma":0.05740009,"domain_scores_codex":[0.9999119,0.00002115061,0.000003619505,0.00002113274,0.00003056549,0.0000117268],"domain_scores_gemma":[0.9998353,0.00003222827,0.00002314465,0.00002038631,0.00007683721,0.00001213944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003650324,0.0002995544,0.2053574,0.0004130474,0.0001375964,0.0007605041,0.0006396161,0.07143359,0.2923062,0.0004233085,0.002922718,0.4249412],"study_design_scores_gemma":[0.00006257302,0.0004241025,0.5436155,0.00004262745,0.0001078368,0.0005925869,0.0009248413,0.4075359,0.0393112,0.000263348,0.007072853,0.00004683022],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674787,0.0002446231,0.02725678,0.0000741609,0.00001948579,0.00009347836,0.001125491,0.0005315964,0.003175706],"genre_scores_gemma":[0.9764157,0.0001607352,0.02226903,0.00003348027,0.000006323857,0.00001992384,0.0004351012,0.00001623933,0.0006436202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02810352,"threshold_uncertainty_score":0.05587989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01308381234515926,"score_gpt":0.2123817357877322,"score_spread":0.199297923442573,"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."}}