{"id":"W3013626641","doi":"10.1002/hyp.13756","title":"Exploring the spatiotemporal variability of the snow water equivalent in a small boreal forest catchment through observation and modelling","year":2020,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; CentrEau - Quebec Water Management Research Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Hydro-Québec","keywords":"Environmental science; Snow; Vegetation (pathology); Precipitation; Hydrology (agriculture); Tree canopy; Sampling (signal processing); Drainage basin; Boreal; Leaf area index; Surface runoff; Canopy; Physical geography; Geography; Meteorology; Ecology; Geology; Cartography","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":[0.0003638441,0.0002382158,0.0001877374,0.0005817289,0.0002898446,0.0005266668,0.0006422042,0.0002975962,0.0003723458],"category_scores_gemma":[0.0005797891,0.0001212489,0.0003335668,0.0009377009,0.0003142222,0.0003037392,0.0002049397,0.0001663148,0.00005092847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002010446,"about_ca_system_score_gemma":0.001277932,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6632751,"about_ca_topic_score_gemma":0.6795207,"domain_scores_codex":[0.9998888,0.00001747117,0.000007785487,0.00004004934,0.00001651016,0.0000294133],"domain_scores_gemma":[0.9998117,0.00007072424,0.00004328043,0.00001621331,0.00003533746,0.00002280359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001310683,0.0002115547,0.7501979,0.0001008297,0.0001805379,0.00052454,0.0006644866,0.2189468,0.006542552,0.0007762489,0.0008626728,0.02086077],"study_design_scores_gemma":[0.00001614941,0.00003104733,0.4744965,0.00001752158,0.00003276825,0.00004551834,0.0003768872,0.5236643,0.00037675,0.000187811,0.0007320821,0.00002274692],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971309,0.00007328223,0.001778214,0.00004017002,0.000002323784,0.00001492778,0.0005187063,0.0000357835,0.0004056423],"genre_scores_gemma":[0.998269,0.00005085755,0.001159059,0.000004990753,0.000002241239,0.00001197929,0.000357608,0.000003247065,0.0001410034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6632751,"threshold_uncertainty_score":0.677416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2133125303140161,"score_gpt":0.2393203395633265,"score_spread":0.02600780924931043,"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."}}