{"id":"W4306291106","doi":"10.1007/s12665-022-10622-3","title":"Combining a geoelectrical survey with integrated groundwater quality data to map the spatial distribution and temporal variations of a leachate plume in a closed landfill (Southern Ontario, Canada)","year":2022,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geoscience BC; Humber Polytechnic; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Plume; Leachate; Borehole; Aquifer; Groundwater; Environmental science; Subsoil; Geology; Piezometer; Hydrology (agriculture); Hydrogeology; Soil science; Geotechnical engineering; Soil water; Engineering; Geography; Waste management; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001501989,0.0001042333,0.0001711275,0.00003259027,0.0004021436,0.00003856421,0.0003727082,0.00001708467,0.0007012738],"category_scores_gemma":[0.0000445122,0.00006307535,0.00001261156,0.0004376514,0.0002030597,0.0001025001,0.0001040385,0.0002341563,0.000004182861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002225537,"about_ca_system_score_gemma":0.0002044145,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9873374,"about_ca_topic_score_gemma":0.9926522,"domain_scores_codex":[0.9979544,0.0006512866,0.0002166545,0.0003481627,0.0005590648,0.0002704136],"domain_scores_gemma":[0.9993381,0.0003238076,0.00008319133,0.000164745,0.000004055951,0.00008612438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001203839,0.00005512325,0.9898167,0.000001294273,0.000005851948,0.000002920593,0.0005097415,0.001104489,0.00003899841,0.000008428876,0.00002280853,0.008313236],"study_design_scores_gemma":[0.0002174829,0.0005581404,0.9819611,0.000002621588,0.00000484034,0.000003184659,0.0004308925,0.01560526,0.00001602149,0.00008956622,0.001005413,0.000105475],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976072,0.00002574067,0.0004582928,0.0004447305,0.0000639673,0.0002010249,0.001170264,0.000004394235,0.00002435842],"genre_scores_gemma":[0.9983885,7.238032e-7,0.0003241318,0.0001158989,0.000008334554,0.000003562117,0.0009738128,0.000001438295,0.0001835921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01450077,"threshold_uncertainty_score":0.7678457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263141643091342,"score_gpt":0.2284494454956145,"score_spread":0.1958180290647011,"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."}}