{"id":"W2801055493","doi":"10.1002/hyp.13129","title":"Comparison of five snow water equivalent estimation methods across categories","year":2018,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Nipissing University; Ministry of Environment; Ministry of the Environment, Conservation and Parks","funders":"","keywords":"Snowpack; Snow; Environmental science; Watershed; Precipitation; Meteorology; Empirical modelling; Energy balance; Climate change; Water balance; Water equivalent; Hydrology (agriculture); Geography; Computer science; Ecology; Engineering; Simulation","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002780275,0.0001073273,0.0002403719,0.000009385687,0.0003162678,0.00003056384,0.0001649854,0.00006643814,0.001873479],"category_scores_gemma":[0.0004536891,0.00005996719,0.000032446,0.0002119501,0.0004283715,0.0001304422,0.00003727267,0.00007087086,0.0001033721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002306541,"about_ca_system_score_gemma":0.00001607015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004190436,"about_ca_topic_score_gemma":0.0007954622,"domain_scores_codex":[0.9990511,0.00005075409,0.0002672189,0.0002033487,0.0001502247,0.000277382],"domain_scores_gemma":[0.9992374,0.0003476166,0.00008390099,0.0001055774,0.0001776339,0.0000479286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002617657,0.0002265295,0.7971474,0.0003311934,0.00009270056,0.000003577447,0.01773147,0.04141105,0.0004159611,0.0001788204,0.001645348,0.1405542],"study_design_scores_gemma":[0.0005037452,0.002210133,0.6650578,0.00003974381,0.00007532681,0.000007459884,0.003890418,0.2316526,0.05125501,0.02066196,0.0241274,0.0005183998],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762774,0.0009205823,0.02049436,0.000465225,0.0001973546,0.0001154443,0.00002236118,0.00005388989,0.001453451],"genre_scores_gemma":[0.9861656,0.00004387556,0.01336483,0.0001464589,0.00009132149,0.000003327487,0.00006143049,0.000001761296,0.0001213717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1902416,"threshold_uncertainty_score":0.9990389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08580541147253669,"score_gpt":0.3829844339563972,"score_spread":0.2971790224838605,"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."}}