{"id":"W1645683239","doi":"10.1002/hyp.9882","title":"Implementation of an automatic calibration procedure for HYDROTEL based on prior OAT sensitivity and complementary identifiability analysis","year":2013,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Hydro-Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Watershed; Calibration; Identifiability; Sensitivity (control systems); Environmental science; Hydrology (agriculture); Temperate climate; Mathematics; Statistics; Computer science; Geology; Ecology; Machine learning; Engineering","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":[0.002795451,0.0009168534,0.0005494735,0.001684292,0.0004377958,0.0008483083,0.0009243768,0.0005569363,0.002942906],"category_scores_gemma":[0.007298503,0.000521071,0.0008671026,0.000530039,0.0005482177,0.0009335335,0.00127079,0.0009635277,0.0006078125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005481082,"about_ca_system_score_gemma":0.001120186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003352175,"about_ca_topic_score_gemma":0.003164455,"domain_scores_codex":[0.9987898,0.0003743246,0.0000744151,0.0002471307,0.0004297083,0.00008460159],"domain_scores_gemma":[0.996869,0.001434033,0.0003549241,0.0005348007,0.0007661426,0.0000410655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002788461,0.0003785051,0.009504048,0.0001658641,0.0002291189,0.00017959,0.0003596566,0.5075265,0.0876608,0.006604427,0.002373973,0.3847387],"study_design_scores_gemma":[0.00001504796,0.00003576252,0.002380571,0.000008444928,0.0000108881,0.00002633904,0.0000160873,0.9727218,0.02299052,0.001095933,0.0006756777,0.00002284187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05688385,0.00002144793,0.9366687,0.0000353271,0.00001097091,0.0001070438,0.00009254064,0.005231629,0.000948386],"genre_scores_gemma":[0.5818064,0.00002750393,0.4160361,0.00005205787,0.00001322008,0.0003235164,0.0003968017,0.0005762292,0.0007681854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003352175,"threshold_uncertainty_score":0.01478398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149211191618349,"score_gpt":0.2694612563444442,"score_spread":0.2545401371826093,"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."}}