{"id":"W2606078809","doi":"10.1002/2017wr020838","title":"The essential value of long‐term experimental data for hydrology and water management","year":2017,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":157,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"European Research Council","keywords":"Wetland; Environmental science; Benchmark (surveying); Environmental resource management; Hydrology (agriculture); Term (time); Foundation (evidence); Process (computing); Hydrological modelling; Water resource management; Computer science; Engineering; Geography; Ecology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.001968145,0.0001212954,0.0001501583,0.00004871915,0.002357299,0.0001533931,0.001751229,0.00005608273,0.0001899658],"category_scores_gemma":[0.00001754789,0.00005926184,0.00003173207,0.00001706622,0.002359951,0.0002028732,0.009002605,0.0001305867,0.0001200782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002295654,"about_ca_system_score_gemma":7.028469e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001931865,"about_ca_topic_score_gemma":0.00008290451,"domain_scores_codex":[0.9980797,0.0001625116,0.0001885812,0.0004939043,0.0003754863,0.0006998082],"domain_scores_gemma":[0.9985061,0.00006360993,0.00003524587,0.001327482,0.000008943194,0.00005867762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007364705,0.001799757,0.5865291,0.001030876,0.003251677,0.0006545639,0.05620475,0.0009187479,0.2371599,0.003085485,0.04421313,0.05778733],"study_design_scores_gemma":[0.004115793,0.001012949,0.1659241,0.00004246043,0.0001379364,0.00001977017,0.001261569,0.004318192,0.4544582,0.006147264,0.3619443,0.0006173819],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878356,0.0001020949,0.00004206332,0.003332759,0.00009293971,0.0006102194,0.000007656154,0.00001015153,0.007966501],"genre_scores_gemma":[0.9939938,0.00009881635,0.00007122454,0.00004034938,0.00005957978,0.0001056591,0.00002133291,0.00001339092,0.005595862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4206049,"threshold_uncertainty_score":0.9990124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0547068126436751,"score_gpt":0.3554055482339929,"score_spread":0.3006987355903178,"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."}}