{"id":"W4367155125","doi":"10.36487/acg_repo/2355_60","title":"Assessing tailings consolidation and changes in supernatant pond area using InSAR and the normalised difference moisture index","year":2023,"lang":"en","type":"article","venue":"Paste/Paste","topic":"Tailings Management and Properties","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Banff Centre; Geomechanica (Canada); University of Alberta","funders":"","keywords":"Tailings; Consolidation (business); Interferometric synthetic aperture radar; Moisture; Index (typography); Environmental science; Geology; Synthetic aperture radar; Remote sensing; Geography; Meteorology; Computer science; Metallurgy; Materials science; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002757909,0.0001650831,0.0001970656,0.0001809038,0.0001131856,0.0002971717,0.00009931506,0.00006176386,0.00001149358],"category_scores_gemma":[0.00004311922,0.0001199931,0.00001401651,0.000269052,0.0001032174,0.0002971978,0.0001220543,0.0001754985,0.000004574119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002213868,"about_ca_system_score_gemma":0.000006665681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001813726,"about_ca_topic_score_gemma":0.0003151412,"domain_scores_codex":[0.9992224,0.00004269217,0.0001733035,0.0001773591,0.0001403952,0.0002438798],"domain_scores_gemma":[0.9996673,0.0001037734,0.00004095205,0.0001287592,0.00002319056,0.00003602387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005441162,0.00004907323,0.5650646,0.002684573,0.0003599135,0.000155841,0.04674253,0.03633059,0.2338468,0.001779951,0.0002755251,0.1121664],"study_design_scores_gemma":[0.002452143,0.00002312404,0.09010218,0.0003656786,0.00005295387,0.0000140285,0.007259779,0.8943844,0.00355465,0.0003009808,0.001064645,0.0004253969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975994,0.0006943253,0.0003499557,0.0003014715,0.0001466403,0.000229328,0.000005120409,0.0001352958,0.0005384254],"genre_scores_gemma":[0.9992074,0.0004407019,0.00005800137,0.00005849761,0.00005439629,0.00001650895,0.00001489853,0.00002357376,0.0001259844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8580539,"threshold_uncertainty_score":0.4893176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02989286159129906,"score_gpt":0.231328713513348,"score_spread":0.2014358519220489,"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."}}