{"id":"W2753828822","doi":"10.1007/s12665-017-6962-5","title":"Statistical framework for scale-up of dispersivity in multi-scale heterogeneous media","year":2017,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Scale (ratio); Scaling; Bootstrapping (finance); Residual; Realization (probability); Sampling (signal processing); Computer science; Statistics; Econometrics; Statistical physics; Mathematics; Algorithm; Physics","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":[0.0003454263,0.0001309996,0.0001917681,0.00002819166,0.0005499877,0.00005906339,0.0004595354,0.00005328863,0.0007112455],"category_scores_gemma":[0.0000694012,0.0001104396,0.00005594404,0.00004237925,0.002364069,0.0003104249,0.0003230115,0.00007529914,0.0001049935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003211388,"about_ca_system_score_gemma":0.000004775718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002479285,"about_ca_topic_score_gemma":0.001963235,"domain_scores_codex":[0.998631,0.00003486348,0.0002173971,0.0003989434,0.0004077737,0.0003100451],"domain_scores_gemma":[0.99938,0.00015529,0.0001251448,0.0002551789,0.000001340911,0.0000830338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001747532,0.0001510072,0.9703188,0.000004691364,0.000003850985,0.000002385332,0.00154617,0.00015067,0.004553836,0.00008828085,0.00001436779,0.02314849],"study_design_scores_gemma":[0.0004248714,0.0001027,0.9879856,0.00001213878,0.000006976969,0.000002228418,0.0005774453,0.001844238,0.007794428,0.0004914153,0.0006116625,0.0001462724],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504757,0.00003393906,0.04862413,0.0001213931,0.0002358134,0.000215992,0.00009422954,0.000006577672,0.0001922337],"genre_scores_gemma":[0.9734579,0.00002166417,0.02600898,0.00004998453,0.00001654862,0.00002626935,0.00000495089,0.000005853517,0.0004078994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02300222,"threshold_uncertainty_score":0.8710513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02773005693070572,"score_gpt":0.2775080598656059,"score_spread":0.2497780029349002,"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."}}