{"id":"W4290995400","doi":"10.2139/ssrn.4183338","title":"Dissolved Phase LNAPL Recovery Under Dynamic Groundwater Table Using Temporal Moment Approach","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Moment (physics); Groundwater; Table (database); Environmental science; Phase (matter); Hydrology (agriculture); Geology; Soil science; Chemistry; Data mining; Computer science; Geotechnical engineering; Physics","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":[0.0001317569,0.0001776389,0.0002584331,0.0002441748,0.0001483642,0.0003664601,0.0003592099,0.0003487442,0.0008844424],"category_scores_gemma":[0.0003389103,0.0001193402,0.0003259696,0.000402457,0.0001883514,0.0005629288,0.0002583434,0.0002067418,0.00008608574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003228585,"about_ca_system_score_gemma":0.0003420891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006285368,"about_ca_topic_score_gemma":0.003657133,"domain_scores_codex":[0.9999565,0.000006554206,0.000002942555,0.00001384322,0.00001013816,0.000009999482],"domain_scores_gemma":[0.9999123,0.00003975915,0.00001685637,0.000007244582,0.00001894096,0.000005018999],"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":[0.0002975764,0.00007383284,0.005819283,0.0001138475,0.00005123766,0.0003745647,0.00006521208,0.925856,0.0399471,0.006261163,0.0004246656,0.02071554],"study_design_scores_gemma":[0.000002790871,0.000008251305,0.0003243258,0.000001038872,0.000004145722,0.000008790031,0.000006645144,0.9969084,0.002368486,0.0002868858,0.00007647855,0.000003824828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7994251,0.0003259121,0.1930573,0.0002382242,0.00005270765,0.00003084265,0.000573419,0.0004703256,0.005826196],"genre_scores_gemma":[0.9932384,0.00009593929,0.00546988,0.00001243472,0.000007835577,0.0000105798,0.0001011943,0.00001784363,0.001045823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006285368,"threshold_uncertainty_score":0.01249754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646106675336456,"score_gpt":0.272326677133552,"score_spread":0.2558656103801874,"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."}}