{"id":"W4401412049","doi":"10.1016/j.chemosphere.2024.143040","title":"Investigating the synergy of rapidly synthesized iron oxide predecessor and plasma-gaseous species for dye-removal to reuse water in irrigation","year":2024,"lang":"en","type":"article","venue":"Chemosphere","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Ministry of Education; Ministry of Science, ICT and Future Planning; National Research Foundation of Korea; Kwangwoon University","keywords":"Reuse; Irrigation; Plasma; Oxide; Environmental science; Environmental chemistry; Chemistry; Water resource management; Environmental engineering; Waste management; Ecology; Organic chemistry; Biology; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002267281,0.0001341367,0.0001753704,0.00001946486,0.00002824591,0.00005192769,0.0001378246,0.00006844853,0.00006397343],"category_scores_gemma":[0.0002168053,0.00009279705,0.00002938883,0.00008098132,0.00005467776,0.0001141421,0.00005964597,0.00005318112,0.00001325789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008890687,"about_ca_system_score_gemma":0.000008080613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001203994,"about_ca_topic_score_gemma":0.00001827989,"domain_scores_codex":[0.9992061,0.00001979134,0.0003009979,0.0001732161,0.0001211035,0.0001788548],"domain_scores_gemma":[0.9994919,0.0002208545,0.00002464259,0.0002066156,0.000007523781,0.00004845453],"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":[0.00001339416,0.000005627911,0.0001412861,0.0003927298,0.0000157155,0.000002747295,0.001227637,0.008616876,0.9879555,0.00007691451,0.0003410099,0.001210625],"study_design_scores_gemma":[0.0002466394,0.00001774561,0.001133265,0.0001723156,0.00001574617,0.000005642036,0.0002190471,0.01002363,0.9850809,0.0003756315,0.002590991,0.0001184379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980928,0.0002806178,0.0001607766,0.0003962884,0.0001730109,0.0004041016,0.00002179017,0.00009400573,0.0003765714],"genre_scores_gemma":[0.9932399,0.0000331691,0.006163348,0.00003328815,0.00005991626,0.00009898542,0.00001781014,0.00004147299,0.0003121369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006002571,"threshold_uncertainty_score":0.3784154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009418826985260679,"score_gpt":0.2033041957950289,"score_spread":0.1938853688097682,"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."}}