{"id":"W4394582009","doi":"10.1139/cgj-2023-0112","title":"Monitoring moisture dynamics in multi-layer cover systems for mine tailings reclamation using autonomous and remote time-lapse electrical resistivity tomography","year":2024,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec en Abitibi-Témiscamingue","funders":"Society of Exploration Geophysicists; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Natural Environment Research Council; Sight Research UK","keywords":"Tailings; Electrical resistivity tomography; Land reclamation; Geotechnical engineering; Electrical resistivity and conductivity; Mining engineering; Geology; Cover (algebra); Moisture; Water content; Environmental science; Engineering; Meteorology; Mechanical engineering; Materials science; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000121344,0.0001963443,0.0001330034,0.0002548025,0.0001341944,0.0002220049,0.0001879422,0.0001968829,0.0002245375],"category_scores_gemma":[0.0001960607,0.0001175448,0.0001020905,0.0002264581,0.0001440163,0.0003078123,0.000194789,0.0001422117,0.00005332112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001734199,"about_ca_system_score_gemma":0.000147612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001329538,"about_ca_topic_score_gemma":0.004018428,"domain_scores_codex":[0.9999394,0.000005223904,0.000002356463,0.0000169511,0.0000250157,0.00001099639],"domain_scores_gemma":[0.9998982,0.00002070009,0.00003470238,0.00001177987,0.00002309588,0.00001147794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001017986,0.00006310236,0.0315363,0.00003463064,0.00001231912,0.0001115405,0.000146451,0.004476114,0.947541,0.00005196692,0.0000557276,0.01586902],"study_design_scores_gemma":[0.00002053521,0.0005869556,0.3121193,0.000009482812,0.00004762656,0.0002208506,0.0004375322,0.1039649,0.5811915,0.0001221166,0.001249598,0.00002966247],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959784,0.0000228895,0.003771438,0.000005948967,0.000001171381,0.000008395968,0.00006036559,0.00004243784,0.0001090221],"genre_scores_gemma":[0.9948777,0.00003495074,0.004892151,0.000004362491,0.000001311252,0.00001154731,0.00005541712,0.000004950228,0.0001174845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001329538,"threshold_uncertainty_score":0.002643585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227984642325229,"score_gpt":0.2589614857761428,"score_spread":0.2366816393528905,"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."}}