{"id":"W4398972358","doi":"10.7910/dvn/t46anr","title":"Replication Data for the manuscript \"Using an ensemble of artificial neural networks to convert snow depth to snow water equivalent over Canada\" submitted to HESS","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Snow; Replication (statistics); Artificial neural network; Computer science; Meteorology; Artificial intelligence; Physical geography; Geography; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001514327,0.00133199,0.0008085396,0.001912539,0.001376613,0.002424383,0.00274314,0.001115461,0.07740824],"category_scores_gemma":[0.008609518,0.000431445,0.001056249,0.004024502,0.0005429788,0.0008600804,0.001512068,0.001415669,0.047559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004948631,"about_ca_system_score_gemma":0.01552403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7090179,"about_ca_topic_score_gemma":0.817832,"domain_scores_codex":[0.9990714,0.0001057563,0.00005740051,0.0001963409,0.0003692341,0.0001999184],"domain_scores_gemma":[0.9948984,0.0004272131,0.0001718381,0.0009318404,0.003177456,0.0003932845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003093069,0.000007924,0.0007407941,0.00008988772,0.0000232918,0.000008928841,0.00001184568,0.0003702856,0.00003198023,0.0002882761,0.9969517,0.001444112],"study_design_scores_gemma":[0.0002984628,0.0000104896,0.0106732,0.0001943413,0.00003277959,0.00002522153,0.0001304685,0.001262981,0.0004001686,0.001066049,0.9858589,0.00004689123],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002746669,0.00004154078,0.0001262269,0.0001596184,0.0001196106,0.00002537616,0.997433,0.0003743248,0.001445598],"genre_scores_gemma":[0.001358808,0.00003628463,0.0003758943,0.00006047143,0.00002024396,0.00007113996,0.9955963,0.0001352616,0.002345608],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2909821,"threshold_uncertainty_score":0.5853915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09627733375795108,"score_gpt":0.2773884127198728,"score_spread":0.1811110789619217,"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."}}