{"id":"W4412816023","doi":"10.18280/ijdne.200603","title":"Enhanced Cu (II) Removal by Iron-Graphite Electrocoagulation: Kinetics, Efficiency and Hydrogen Co-Production","year":2025,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Baghdad","keywords":"Electrocoagulation; Kinetics; Hydrogen production; Graphite; Hydrogen; Production (economics); Metallurgy; Chemical engineering; Materials science; Chemistry; Nuclear chemistry; Engineering; Physics; Organic chemistry; Physical chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002160111,0.0001232601,0.0001492022,0.0002432389,0.00004911948,0.00004207902,0.0001369869,0.0001401338,0.00001078981],"category_scores_gemma":[0.00006141159,0.0001202989,0.00003490708,0.0001027452,0.00004449038,0.0001968725,0.00002159953,0.0002064384,9.012447e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001183992,"about_ca_system_score_gemma":0.00001326772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.524009e-7,"about_ca_topic_score_gemma":0.000001510568,"domain_scores_codex":[0.9991351,0.00002560933,0.0003576439,0.0001224973,0.0002479404,0.000111219],"domain_scores_gemma":[0.9996322,0.0000497641,0.0001362232,0.0000635488,0.00007321109,0.00004509384],"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":[0.00007070743,0.00003791654,0.0002346179,0.00001483827,0.0001065387,0.000006262034,0.00008103577,0.01506292,0.9799349,0.0002402718,0.0005161279,0.00369382],"study_design_scores_gemma":[0.001279763,0.0003040578,0.003512485,0.0001651804,0.0001076909,0.0003921048,0.00005730419,0.04496913,0.9409485,0.004052851,0.003830182,0.0003807798],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9243954,0.002691685,0.07122856,0.000430877,0.0009272327,0.0001105469,0.00001109946,0.00002272858,0.0001818743],"genre_scores_gemma":[0.9948689,0.002372548,0.002356357,0.00008489996,0.0001716731,0.000001865076,0.00002126674,0.00001283526,0.0001096757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07047348,"threshold_uncertainty_score":0.4905648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003505626501044789,"score_gpt":0.2189709305621646,"score_spread":0.2154653040611198,"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."}}