{"id":"W2898451198","doi":"10.1017/jog.2018.78","title":"SHMIP The subglacial hydrology model intercomparison Project","year":2018,"lang":"en","type":"article","venue":"Journal of Glaciology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; Centro Svizzero di Calcolo Scientifico; National Science Foundation; University of Cambridge; Leibniz-Gemeinschaft; National Supercomputing Centre Singapore; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada Research Chairs; Laboratory Directed Research and Development; Agence Nationale de la Recherche; National Aeronautics and Space Administration","keywords":"Hydrology (agriculture); Groundwater recharge; Geology; Drainage; Hydrological modelling; Water pressure; Environmental science; Climatology; Petroleum engineering; Groundwater; Geotechnical engineering; Aquifer","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004391219,0.00007931961,0.0002146453,0.00003602429,0.0002038422,0.00001313299,0.0002816301,0.00006588536,0.0003787564],"category_scores_gemma":[0.0001192351,0.00004271864,0.00008370273,0.0001248155,0.0003463123,0.00008130688,0.00001846846,0.00021822,0.00005285929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000487996,"about_ca_system_score_gemma":0.00008725689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001959311,"about_ca_topic_score_gemma":0.002993793,"domain_scores_codex":[0.9991791,0.000104471,0.0003230898,0.00008736417,0.00009876685,0.0002072643],"domain_scores_gemma":[0.9993091,0.0001960531,0.0002261531,0.0001015571,0.0001325507,0.00003454278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004050643,0.0000289336,0.9440676,0.000003519706,0.000105556,0.00001189232,0.002872011,0.003906877,0.00006691725,0.0005607393,0.03463932,0.0133316],"study_design_scores_gemma":[0.0005629103,0.001951305,0.753776,0.000007369871,0.00005936667,0.0002924936,0.001188497,0.125045,0.00002222549,0.004800196,0.1121578,0.0001368752],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894014,0.0005732407,0.003621909,0.002653107,0.001115231,0.00006434815,0.000004176893,0.000006511776,0.002560061],"genre_scores_gemma":[0.9967034,0.00008276669,0.001073329,0.001059747,0.0009772779,3.259748e-7,0.000002056936,0.000001748309,0.00009932381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1902916,"threshold_uncertainty_score":0.4147117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04765832457879458,"score_gpt":0.2810355431168838,"score_spread":0.2333772185380892,"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."}}