{"id":"W1996600115","doi":"10.1016/j.jenvman.2013.11.036","title":"Removal of copper in leachate from mining residues using electrochemical technology","year":2013,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Environmental Protection Agency","keywords":"Copper; Electrolysis; Leachate; Cathode; Intensity (physics); Factorial experiment; Current (fluid); Electrochemistry; Electrode; Central composite design; Leaching (pedology); Response surface methodology; Chemistry; Materials science; Environmental science; Metallurgy; Electrolyte; Environmental chemistry; Chromatography; Soil science; Computer science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001089653,0.00009145729,0.0001760625,0.0002209095,0.00001285357,0.000009081417,0.0001134739,0.00005536607,0.0001962865],"category_scores_gemma":[0.000004047852,0.00008305596,0.00005140008,0.00007328795,0.00002993453,0.0001448598,0.00004496442,0.0001895034,0.00001082455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001335592,"about_ca_system_score_gemma":0.000001417085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007136509,"about_ca_topic_score_gemma":0.000001350446,"domain_scores_codex":[0.9992298,0.0000160586,0.0003866302,0.00007475531,0.0001572545,0.0001355031],"domain_scores_gemma":[0.9997541,0.00001256705,0.0001144836,0.00008380504,0.000002513744,0.00003257241],"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":[0.00001241906,0.00005727358,0.002213948,0.00001584393,0.00007768845,0.00007002882,0.00005948334,0.001463557,0.9799968,0.00002121983,0.00007462158,0.0159371],"study_design_scores_gemma":[0.004713293,0.0004902495,0.1689773,0.001044307,0.0003337746,0.001158442,0.01168549,0.0437839,0.7475946,0.002622733,0.01645121,0.001144725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969913,0.0008647972,0.0007065998,0.00005212836,0.00009939995,0.00007126729,8.089467e-7,0.0000100147,0.001203669],"genre_scores_gemma":[0.9810786,0.0002975499,0.01848834,0.00001976336,0.00003505094,0.000001116572,8.991362e-7,0.00001215117,0.0000665648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2324022,"threshold_uncertainty_score":0.3386924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00677205554398149,"score_gpt":0.1950498477125286,"score_spread":0.1882777921685471,"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."}}