{"id":"W2064571432","doi":"10.1016/j.wasman.2013.05.020","title":"Estimating water content in an active landfill with the aid of GPR","year":2013,"lang":"en","type":"article","venue":"Waste Management","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Guelph; Regional Municipality of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ground-penetrating radar; Borehole; Water content; Offset (computer science); Environmental science; Groundwater; Soil science; Environmental engineering; Radar; Geology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004674411,0.0000536277,0.00006721311,0.00001789132,0.00001619546,0.00001330629,0.00008206921,0.000007594136,0.00002423542],"category_scores_gemma":[5.571076e-7,0.00002724752,0.00001104176,0.00004667574,0.00001357657,0.00004249838,0.00003046413,0.00004218987,0.00002380527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009208184,"about_ca_system_score_gemma":3.087283e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005404,"about_ca_topic_score_gemma":0.00001064767,"domain_scores_codex":[0.999688,0.00001205919,0.00007322172,0.00006754218,0.00005726747,0.0001018915],"domain_scores_gemma":[0.9998003,0.00001173933,0.00001052824,0.0001485571,0.00001232833,0.00001660354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002760894,0.0003196327,0.0007452039,0.0005311543,0.0002792138,0.000006433692,0.005188884,0.3631657,0.09667026,0.01131646,0.0008893695,0.5208601],"study_design_scores_gemma":[0.00246819,0.0003681238,0.1505453,0.0002716951,0.0001324616,0.000001378967,0.01743081,0.7231894,0.09333428,0.007008177,0.004504494,0.0007456693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836239,0.000002841278,0.01014391,0.00027177,0.00001850043,0.0003454619,9.950182e-7,0.0000240056,0.005568645],"genre_scores_gemma":[0.9800072,6.102831e-7,0.01961,0.00003116476,0.00001682437,0.0001804767,0.000002259628,0.000007644425,0.0001438529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5201144,"threshold_uncertainty_score":0.1111122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01802278997607771,"score_gpt":0.2249962886735063,"score_spread":0.2069734986974286,"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."}}