{"id":"W4385420812","doi":"10.3390/w15152769","title":"Implications of Extended Environmental Multimedia Modeling System (EEMMS) on Water Allocation Management: Tritium Numerical Case Study","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Anhui University; National Natural Science Foundation of China","keywords":"Tritium; Groundwater; Pollutant; Environmental science; Tritiated water; Vadose zone; Tritium illumination; Environmental engineering; Chemistry; Geology; Nuclear physics; Geotechnical 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":[0.001045412,0.0005119282,0.0004947626,0.0004821343,0.0006941063,0.001055282,0.0008059872,0.001547739,0.001739438],"category_scores_gemma":[0.001987596,0.000257027,0.0006156919,0.0008442562,0.0007070171,0.001015299,0.0009864287,0.0005600242,0.0001156279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001454208,"about_ca_system_score_gemma":0.001105717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02453485,"about_ca_topic_score_gemma":0.01722533,"domain_scores_codex":[0.9995302,0.0002478281,0.00002287444,0.00006047558,0.00007059026,0.00006816085],"domain_scores_gemma":[0.9990037,0.0006807745,0.00006831857,0.00007575862,0.0001194869,0.00005200834],"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":[0.00009289067,0.00005654948,0.003592425,0.00003371388,0.0000158122,0.0003531726,0.00005324565,0.9836565,0.001573302,0.003607296,0.0004107184,0.006554408],"study_design_scores_gemma":[0.00002458638,0.00005589842,0.0006512016,0.000006359406,0.00001384114,0.00003413546,0.00007870952,0.9948755,0.001605732,0.001738955,0.0009036359,0.00001148657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9010724,0.0003441501,0.08128063,0.00131682,0.00008385359,0.0001416111,0.0009264409,0.0004775008,0.01435641],"genre_scores_gemma":[0.9777644,0.0001836913,0.01983383,0.00005906034,0.00001352315,0.00008101436,0.0001863433,0.00003507827,0.001842973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02453485,"threshold_uncertainty_score":0.04878408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01978943601537032,"score_gpt":0.2407743348798186,"score_spread":0.2209848988644483,"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."}}