{"id":"W4386118921","doi":"10.22541/essoar.169290547.74870875/v1","title":"Tradeoffs  between temporal and spatial pattern calibration and their impacts on robustness  and transferability of hydrologic model parameters to ungauged basins            ","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi; Istanbul Teknik Üniversitesi; Ulusal Yüksek Başarımlı Hesaplama Merkezi, Istanbul Teknik Üniversitesi; Villum Fonden","keywords":"Robustness (evolution); Evapotranspiration; Calibration; Structural basin; Equifinality; Spatial ecology; Computer science; Environmental science; Transferability; Hydrological modelling; Spatial variability; Parametric statistics; Statistics; Machine learning; Geology; Mathematics; Artificial intelligence; Climatology","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":[],"consensus_categories":[],"category_scores_codex":[0.005110234,0.0006390751,0.0003693037,0.00040113,0.0002552638,0.0007999502,0.0006592794,0.0006699887,0.0006499308],"category_scores_gemma":[0.02183594,0.0003981552,0.0006171483,0.000445793,0.0006375284,0.001697187,0.001340072,0.0007539525,0.0000994419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005353584,"about_ca_system_score_gemma":0.0004367016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002048818,"about_ca_topic_score_gemma":0.001438521,"domain_scores_codex":[0.9986019,0.0007253808,0.0001245357,0.0002412233,0.0001950671,0.0001119451],"domain_scores_gemma":[0.9900798,0.006543346,0.001035911,0.001819728,0.0004278765,0.00009348022],"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":[0.0002112756,0.0001276208,0.01837643,0.0000668518,0.0001294687,0.00007005635,0.00009542066,0.9407176,0.007739637,0.00114815,0.00009272792,0.0312248],"study_design_scores_gemma":[0.00005250401,0.0003749723,0.02625962,0.00003452886,0.00006412346,0.00009190484,0.0001185661,0.9527764,0.01714812,0.002567842,0.0004719448,0.0000395733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8630655,0.000184795,0.1342869,0.0002268133,0.00001577479,0.0000794864,0.0001096303,0.0003177371,0.001713488],"genre_scores_gemma":[0.9920695,0.0000361611,0.007569842,0.00002436774,0.000002876224,0.00004057745,0.00007082626,0.00003095965,0.0001549754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005110234,"threshold_uncertainty_score":0.02702582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04773889390859255,"score_gpt":0.2485439277302741,"score_spread":0.2008050338216816,"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."}}