{"id":"W1990369357","doi":"10.1016/j.jhydrol.2014.04.031","title":"Integration of hydrological and geophysical data beyond the local scale: Application of Bayesian sequential simulation to field data from the Saint-Lambert-de-Lauzon site, Québec, Canada","year":2014,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Lausanne; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Scale (ratio); Field (mathematics); Bayesian probability; Aquifer; Data integration; Environmental science; Geology; Hydrology (agriculture); Groundwater; Computer science; Data mining; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001962594,0.0004337512,0.0005028012,0.0007832428,0.0009407879,0.0009038765,0.001106587,0.0005772355,0.001294108],"category_scores_gemma":[0.006278209,0.0004941939,0.0004808288,0.00145742,0.0005111241,0.000662837,0.0003807984,0.0005954683,0.0001492384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008044804,"about_ca_system_score_gemma":0.009580076,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9418512,"about_ca_topic_score_gemma":0.9299316,"domain_scores_codex":[0.9996747,0.0001184263,0.00002196957,0.00007465583,0.00006361205,0.00004668253],"domain_scores_gemma":[0.997367,0.001569546,0.0001217412,0.0001351271,0.0006852562,0.000121332],"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":[0.0001073931,0.0001368213,0.02672051,0.00002731795,0.00006593734,0.00005095111,0.00008876038,0.9550285,0.000475423,0.0007156,0.0006850598,0.01589782],"study_design_scores_gemma":[0.00001652413,0.000007967264,0.006171374,0.00000291017,0.00001016775,0.000004060891,0.00003342882,0.9932216,0.0001073179,0.0002502002,0.0001663384,0.000007989535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674732,0.0001122111,0.02796043,0.0004249718,0.00001963502,0.0001188547,0.001290405,0.0004176421,0.002182757],"genre_scores_gemma":[0.9797699,0.0000678034,0.01813691,0.00003143561,0.00000603783,0.00005359415,0.00103927,0.00006151636,0.0008334152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0581488,"threshold_uncertainty_score":0.1169825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01363705494102039,"score_gpt":0.2479346839531133,"score_spread":0.2342976290120929,"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."}}