{"id":"W4385761293","doi":"10.5194/hess-2023-143-supplement","title":"Supplementary material to \"On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow\"","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Snow; Environmental science; Distributed element model; Computer science; Hydrology (agriculture); Remote sensing; Geology; Meteorology; Geography; Geotechnical engineering; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001787614,0.002381428,0.001464687,0.001242638,0.000400227,0.001150392,0.002404544,0.002215236,0.4402437],"category_scores_gemma":[0.01695011,0.0007884199,0.001198036,0.002160311,0.0003535529,0.001997474,0.002063207,0.001485725,0.06385284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007404195,"about_ca_system_score_gemma":0.001441725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00420055,"about_ca_topic_score_gemma":0.004829335,"domain_scores_codex":[0.999229,0.0002816473,0.00006739445,0.0001166549,0.0002371102,0.0000681704],"domain_scores_gemma":[0.992007,0.005209637,0.0002171032,0.0009482098,0.001433944,0.0001841642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002497106,0.0002934448,0.0005762417,0.001187585,0.0001676697,0.000279249,0.00005019289,0.04402184,0.001654322,0.03815281,0.8570397,0.05632725],"study_design_scores_gemma":[0.0008500248,0.0002514488,0.00425818,0.0004156905,0.00009742576,0.0004841342,0.0001145719,0.4456332,0.00687489,0.2150003,0.325855,0.0001652754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.01307908,0.002590165,0.529074,0.0100364,0.01853064,0.0006125041,0.3260572,0.01340855,0.08661155],"genre_scores_gemma":[0.1192746,0.003204192,0.3961486,0.003991625,0.008663547,0.001724225,0.3558075,0.0167277,0.09445795],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.4402437,"threshold_uncertainty_score":0.7984244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02872303822122712,"score_gpt":0.2261836248345582,"score_spread":0.1974605866133311,"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."}}