{"id":"W2789024737","doi":"10.1520/gtj20170035","title":"Characterization of Self-Weight Consolidation of Fine-Grained Mine Tailings Using Moisture Sensors","year":2018,"lang":"en","type":"article","venue":"Geotechnical Testing Journal","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Tailings; Consolidation (business); Geotechnical engineering; Geology; Water content; Moisture; Mining engineering; Materials science; Composite material; Metallurgy","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":[0.0003157147,0.000115945,0.0002327408,0.00008362256,0.00008775283,0.00001565971,0.0001272209,0.0001034884,0.00001802066],"category_scores_gemma":[0.0005068604,0.0001063549,0.00006556261,0.0004779265,0.0001096333,0.00008034045,0.00002784844,0.0002592769,0.000002711162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002650428,"about_ca_system_score_gemma":0.00002600174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005756476,"about_ca_topic_score_gemma":2.641939e-7,"domain_scores_codex":[0.9990267,0.00003865517,0.0004918031,0.000107402,0.0001635635,0.0001719049],"domain_scores_gemma":[0.9989684,0.0001724716,0.0002523754,0.0001566637,0.0003766672,0.00007338851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003780121,0.00003932505,0.000195338,0.00003802386,0.00001803994,8.170253e-7,0.0000448266,0.002321626,0.9937614,0.000190676,0.00001021793,0.003375909],"study_design_scores_gemma":[0.0005486482,0.0002396069,0.02215519,0.0003674803,0.0001332129,0.000185972,0.00001805266,0.5278606,0.4444147,0.002994471,0.0007586113,0.0003234507],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8721524,0.000009289408,0.1273647,0.00006091574,0.00007948613,0.00009199735,0.000007739786,0.0001209274,0.0001125029],"genre_scores_gemma":[0.838165,0.000004644495,0.1614602,0.000007369737,0.0003326535,0.000002069067,0.000004771782,0.00001762284,0.000005619699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5493467,"threshold_uncertainty_score":0.4337026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0260261652165931,"score_gpt":0.2671167119066494,"score_spread":0.2410905466900563,"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."}}