{"id":"W4205177262","doi":"10.1111/gwmr.12497","title":"Predicting Vertical <scp>LNAPL</scp> Distribution in the Subsurface under the Fluctuating Water Table Effect","year":2022,"lang":"en","type":"article","venue":"Groundwater Monitoring & Remediation","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep de Chicoutimi; Université du Québec à Chicoutimi","funders":"Singapore Institute of Manufacturing Technology; Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Water table; Aquifer; Groundwater; Elevation (ballistics); Table (database); Soil science; Water well; Geology; Range (aeronautics); Hydrology (agriculture); Environmental science; Geotechnical engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002169588,0.000315674,0.0002000673,0.0003880021,0.0001465801,0.0004251559,0.000365582,0.0002819994,0.0002954479],"category_scores_gemma":[0.0007140539,0.0001792551,0.0002159,0.0003628551,0.0001739815,0.0005237432,0.0002302508,0.0002068088,0.0000856855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000373018,"about_ca_system_score_gemma":0.0005234882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249444,"about_ca_topic_score_gemma":0.0132065,"domain_scores_codex":[0.9999171,0.00001170953,0.000004888117,0.00002773779,0.00002599202,0.00001249231],"domain_scores_gemma":[0.999809,0.0000692937,0.00005101646,0.00001404938,0.00004379315,0.00001294348],"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":[0.0001489665,0.0001121928,0.1897322,0.00006876385,0.00004001133,0.0001456126,0.00005998879,0.695217,0.06519774,0.0002695737,0.0001429642,0.0488651],"study_design_scores_gemma":[0.000004307715,0.00004590604,0.02776323,0.000002955349,0.000007237177,0.00001441203,0.0000436116,0.9631588,0.008764201,0.0001080283,0.00007848839,0.000008750842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9379475,0.00004374945,0.0610684,0.00002253529,0.00000410231,0.00001684873,0.0002477368,0.0002302331,0.0004188214],"genre_scores_gemma":[0.9936751,0.0000226944,0.006132395,0.000002826996,0.000001270776,0.000007461906,0.00008624219,0.000005880547,0.00006615432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01249444,"threshold_uncertainty_score":0.02484345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063570436542012,"score_gpt":0.2201701832824173,"score_spread":0.2095344789169972,"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."}}