{"id":"W4410362627","doi":"10.1061/jggefk.gteng-14332","title":"On the Potential of Nuclear Magnetic Resonance for Assessing Water Content and Saturation in Mine Tailings","year":2025,"lang":"en","type":"preprint","venue":"Journal of Geotechnical and Geoenvironmental Engineering","topic":"Geoscience and Mining Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tailings; Saturation (graph theory); Water saturation; Environmental science; Mining engineering; Nuclear magnetic resonance; Geology; Waste management; Materials science; Engineering; Geotechnical engineering; Metallurgy; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001423827,0.0005119814,0.0002510805,0.0005876421,0.0002950091,0.0006786657,0.0004042687,0.0006825514,0.000736339],"category_scores_gemma":[0.002541106,0.0002423141,0.0002432625,0.0003450614,0.0006700487,0.0008082338,0.0005648989,0.0004477965,0.0002849167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002816691,"about_ca_system_score_gemma":0.0004349899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001774506,"about_ca_topic_score_gemma":0.005021305,"domain_scores_codex":[0.9995269,0.0001635357,0.00001555353,0.00009296776,0.000166816,0.00003428191],"domain_scores_gemma":[0.9982542,0.001125759,0.0001328968,0.00007532328,0.0003589093,0.00005282116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004149952,0.00004336028,0.007935774,0.0002179802,0.000029439,0.0001286494,0.0001548724,0.004161894,0.941602,0.0002305916,0.0001342857,0.04494618],"study_design_scores_gemma":[0.00001722282,0.00140238,0.02306669,0.0001194664,0.0001856087,0.0005147243,0.0005109474,0.04511659,0.9244584,0.0007751696,0.003734068,0.0000987111],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9433198,0.004397764,0.04717547,0.0004660479,0.0000490613,0.00006153989,0.0001700451,0.0002413984,0.004118882],"genre_scores_gemma":[0.9750305,0.001914302,0.02157862,0.0001301965,0.00002095248,0.00003342344,0.00008728345,0.00003882011,0.001165951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001774506,"threshold_uncertainty_score":0.007529974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007809598156057167,"score_gpt":0.1869001570453565,"score_spread":0.1790905588892993,"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."}}