{"id":"W3007040014","doi":"10.1038/s41597-019-0346-5","title":"Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater","year":2020,"lang":"en","type":"article","venue":"Scientific Data","topic":"Karst Systems and Hydrogeology","field":"Earth and Planetary Sciences","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Karst; Aquifer; Groundwater; Hydrograph; Hydrology (agriculture); Spring (device); Water resource management; Environmental science; Groundwater flow; Environmental resource management; Geography; Geology; Cartography; Drainage basin; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004739526,0.0006895895,0.0005312349,0.002289739,0.0003008902,0.0006518324,0.001065107,0.0007315393,0.006950894],"category_scores_gemma":[0.002307203,0.0002124708,0.0006369104,0.004627769,0.0001988021,0.0008892891,0.0009571972,0.0005623426,0.00519019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000718613,"about_ca_system_score_gemma":0.001680834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03616762,"about_ca_topic_score_gemma":0.05103355,"domain_scores_codex":[0.9995729,0.00005239742,0.00009536261,0.0001067992,0.0001130939,0.00005942629],"domain_scores_gemma":[0.9990439,0.0001744684,0.0001516645,0.0001967542,0.0003339671,0.00009936257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001426316,0.00008816599,0.0410866,0.001056291,0.0001423009,0.000204554,0.0001847028,0.004492485,0.001102522,0.001740207,0.9270036,0.02275606],"study_design_scores_gemma":[0.000277081,0.00004202724,0.1310703,0.0003304565,0.00007860638,0.0002255598,0.0006270682,0.01484481,0.001590235,0.003546291,0.8472704,0.00009723343],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005354446,0.00009700986,0.000524769,0.0001513038,0.00002998578,0.00002689056,0.9919454,0.000653796,0.001216373],"genre_scores_gemma":[0.007500047,0.00006891375,0.001060967,0.00002864963,0.00001047056,0.0000686384,0.9908754,0.00004332902,0.0003435811],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03616762,"threshold_uncertainty_score":0.0719142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1614700026526175,"score_gpt":0.3372273776496487,"score_spread":0.1757573749970312,"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."}}