{"id":"W7133271466","doi":"","title":"Executive Summary of Groundwater science relevant to the Great Lakes Water Quality: A status report","year":2016,"lang":"en","type":"other","venue":"Federal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"U.S. Geological Survey; Fisheries and Oceans Canada; Environment and Climate Change Canada; Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources; U.S. Environmental Protection Agency","keywords":"Executive summary; Groundwater; Hydrology (agriculture); Work (physics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008544569,0.001510649,0.001416711,0.006786865,0.002960382,0.006923817,0.003052906,0.005259807,0.06193858],"category_scores_gemma":[0.0108756,0.0009233688,0.001104635,0.00880548,0.00103968,0.002148316,0.002017219,0.002730624,0.04434726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01143952,"about_ca_system_score_gemma":0.1391582,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6112182,"about_ca_topic_score_gemma":0.6418313,"domain_scores_codex":[0.9933769,0.0002604014,0.0002801912,0.000256959,0.005062024,0.0007635756],"domain_scores_gemma":[0.9558539,0.001243521,0.001309866,0.000651766,0.03716535,0.003775669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00004212853,0.00002909616,0.0003354136,0.0002529226,0.000005200569,0.00002188655,0.00001547117,0.0001182196,0.0001788216,0.0005407254,0.9865038,0.01195621],"study_design_scores_gemma":[0.00003199973,0.00002217246,0.003792921,0.0003246331,0.00002021574,0.000008546989,0.00006340172,0.0001259331,0.0002728694,0.0003294624,0.9949924,0.00001535739],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.003896813,0.01714921,0.003547753,0.08666886,0.03390613,0.01017427,0.2787599,0.002140481,0.5637566],"genre_scores_gemma":[0.007204192,0.02502959,0.005049424,0.01548653,0.005195951,0.002574475,0.09932205,0.0007048393,0.839433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3887818,"threshold_uncertainty_score":0.7821429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01277027658850438,"score_gpt":0.263173032669348,"score_spread":0.2504027560808436,"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."}}