{"id":"W4212789837","doi":"10.5194/hess-26-795-2022","title":"Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling","year":2022,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Streamflow; Computer science; Sensitivity (control systems); Hydrological modelling; Climatology; Temporal scales; Environmental science; Drainage basin; Geology; Cartography; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002723368,0.00008356581,0.0001565483,0.00005310321,0.0008312166,0.000008011606,0.0001091372,0.00004869128,0.00008631912],"category_scores_gemma":[0.0000109284,0.00007075996,0.00002822068,0.00009541561,0.0009338578,0.0001293699,0.0002002081,0.00007546417,0.000001435097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002100216,"about_ca_system_score_gemma":0.000008685905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002598913,"about_ca_topic_score_gemma":0.00002693768,"domain_scores_codex":[0.9987193,0.0002685386,0.000200795,0.0003366057,0.0002807264,0.0001940306],"domain_scores_gemma":[0.9996653,0.000148441,0.00008490682,0.00005884067,0.00001182724,0.00003063722],"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":[0.00007384179,0.00001698208,0.05465337,0.00001339827,0.00002418443,0.000001250456,0.000378929,0.9413071,0.00003750929,0.001127436,0.00004297248,0.002322973],"study_design_scores_gemma":[0.0002351668,0.0002914242,0.0149932,0.0000053061,0.00005133208,0.00003663499,0.0002764445,0.9830538,0.000005273968,0.0009378487,0.00004317626,0.00007039692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701025,0.0005960682,0.02720772,0.0003082314,0.0001527309,0.0003658461,0.00000285819,0.00001560569,0.001248372],"genre_scores_gemma":[0.9994144,0.00002395507,0.0002468316,0.0001317359,0.00002040219,0.0001208438,0.000008303313,0.000002164953,0.00003129854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04174664,"threshold_uncertainty_score":0.639313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946648598697775,"score_gpt":0.2586038152592142,"score_spread":0.2191373292722364,"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."}}