{"id":"W3211052921","doi":"10.21203/rs.3.rs-952261/v1","title":"Assessment of Snowmelt Quality Discharging from a Cold-Climate Urban Landscape During Spring Melt","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Global Water Futures; Canada First Research Excellence Fund","keywords":"Snowmelt; Spring (device); Environmental science; Cold climate; Physical geography; Hydrology (agriculture); Quality (philosophy); Snow; Atmospheric sciences; Climatology; Meteorology; Geography; Geology; Engineering; Geotechnical engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001297713,0.0002135664,0.0003563639,0.0006148032,0.0006082861,0.0006705917,0.0002285139,0.0004384765,0.0007505878],"category_scores_gemma":[0.0001793695,0.000131847,0.0003923729,0.0005974515,0.0002346718,0.0002866441,0.0002730867,0.000176469,0.0001483635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00036552,"about_ca_system_score_gemma":0.0002436051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01782807,"about_ca_topic_score_gemma":0.02823955,"domain_scores_codex":[0.9999194,0.000007041408,0.000004706891,0.00002225025,0.00002589425,0.00002075869],"domain_scores_gemma":[0.9999084,0.00001744938,0.000014625,0.000005428275,0.00003089111,0.00002312962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00261135,0.0003098462,0.7528468,0.0001635172,0.0001802944,0.00162799,0.001561156,0.01111721,0.2095346,0.000152436,0.0004800952,0.01941479],"study_design_scores_gemma":[0.00001986939,0.0002734261,0.9719436,0.000006811981,0.00005023029,0.000094996,0.000966779,0.01569645,0.0103917,0.00006824169,0.0004764695,0.00001153191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993348,0.000008854605,0.0001651505,0.000003673022,0.000001506339,0.000005118718,0.0002040765,0.000009525332,0.0002673833],"genre_scores_gemma":[0.9991499,0.00001512925,0.0001886083,0.000002617024,0.000001989329,0.000004099202,0.000363174,0.000006089739,0.0002682291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01782807,"threshold_uncertainty_score":0.03544861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0372014906023909,"score_gpt":0.368340800393013,"score_spread":0.3311393097906221,"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."}}