{"id":"W4400195718","doi":"10.2139/ssrn.4881394","title":"Novel Nitrogen-Rich Hydrogel Absorbent for Selective Extraction of Rare Earth Elements from Wastewater","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Rare earth; Extraction (chemistry); Wastewater; Nitrogen; Earth (classical element); Chemistry; Pulp and paper industry; Environmental chemistry; Environmental science; Waste management; Chromatography; Environmental engineering; Organic chemistry; Mineralogy; Engineering; Mathematics","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005252591,0.0003625617,0.0003927222,0.0002905914,0.0001018657,0.0001129778,0.0002392328,0.0003061007,0.00007230872],"category_scores_gemma":[0.00003059523,0.0003511043,0.0002712871,0.0001549811,0.00001663012,0.0001692166,0.00006950289,0.003220941,0.00003841896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008045807,"about_ca_system_score_gemma":0.001207482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004230338,"about_ca_topic_score_gemma":0.0005773464,"domain_scores_codex":[0.9973972,0.00002097219,0.0007487573,0.0003481253,0.0003501539,0.001134777],"domain_scores_gemma":[0.9991139,0.00006491569,0.000284219,0.0001913069,0.0002622555,0.00008346753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008843351,0.0009899157,0.0008626099,0.002610092,0.01536296,0.00001492033,0.003709851,0.1585332,0.7608026,0.0243094,0.008160072,0.02376005],"study_design_scores_gemma":[0.001649604,0.0003186997,0.00007793291,0.0003946098,0.0006632708,0.0002869519,0.001694256,0.0304747,0.3222311,0.6325787,0.008718327,0.0009117603],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8860535,0.01597098,0.09121127,0.0001659031,0.002808414,0.00088927,0.00039257,0.0003309023,0.002177174],"genre_scores_gemma":[0.9927126,0.003627741,0.001288332,0.00001633926,0.0006362062,0.00009659147,0.0001895737,0.0001079354,0.00132464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6082693,"threshold_uncertainty_score":0.9998941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322751307989329,"score_gpt":0.2660194204131399,"score_spread":0.2527919073332466,"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."}}