{"id":"W2772829255","doi":"10.1109/igarss.2017.8127223","title":"A first overview of SnowEx ground-based remote sensing activities during the winter 2016–2017","year":2017,"lang":"en","type":"article","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of Waterloo","funders":"","keywords":"Snow; Remote sensing; Transect; Environmental science; Meteorology; Geology; Geography","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":[0.0007137249,0.0005326487,0.0002416092,0.003367437,0.0005019777,0.0009893634,0.0002954612,0.0002625946,0.002729352],"category_scores_gemma":[0.0006063993,0.0001893294,0.0002199796,0.004064523,0.0001457429,0.0009549377,0.0007018201,0.0002188841,0.001393348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007412844,"about_ca_system_score_gemma":0.001024281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03942131,"about_ca_topic_score_gemma":0.07487328,"domain_scores_codex":[0.9996234,0.00002931992,0.00005207402,0.00008357798,0.0001423723,0.00006913683],"domain_scores_gemma":[0.999176,0.00006744211,0.0001392887,0.00006251859,0.0004654167,0.00008948238],"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":[0.0004904018,0.0001407857,0.324345,0.002529598,0.0001822005,0.0005846653,0.003059821,0.002974762,0.02443454,0.001700822,0.199651,0.4399064],"study_design_scores_gemma":[0.00001323513,0.00009865793,0.4427741,0.0006019949,0.0000495077,0.0002915951,0.001919835,0.0015812,0.00471659,0.0002369262,0.5476807,0.00003569696],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4915518,0.03620365,0.01414659,0.002226705,0.001268697,0.0009597114,0.325598,0.003946208,0.1240987],"genre_scores_gemma":[0.468552,0.02286541,0.02769798,0.0007766508,0.0008395539,0.0007513471,0.4411977,0.0008565079,0.0364628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03942131,"threshold_uncertainty_score":0.07838374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05743564977890288,"score_gpt":0.2585640966272203,"score_spread":0.2011284468483174,"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."}}