{"id":"W4400093401","doi":"10.1021/acs.iecr.4c01190","title":"Process for the Remediation of Titanogypsum (Red Gypsum) Using Weak Acid and CaCl<sub>2</sub> to Produce Saleable α-Gypsum and FeCl<sub>2</sub>·4H<sub>2</sub>O","year":2024,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Gypsum; Environmental remediation; Process (computing); Chemistry; Mineralogy; Nuclear chemistry; Inorganic chemistry; Waste management; Metallurgy; Materials science; Contamination; Computer science; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.002181544,0.0004759231,0.000499971,0.0003267216,0.0002780945,0.0003070287,0.0003536386,0.0005550745,0.000007246777],"category_scores_gemma":[0.00165712,0.0004367463,0.0001235777,0.001170966,0.000114469,0.0003855911,0.0001664519,0.001663297,0.000009096803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003205458,"about_ca_system_score_gemma":0.0002489178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001228786,"about_ca_topic_score_gemma":0.000007399581,"domain_scores_codex":[0.9967117,0.00006349933,0.000683206,0.0007695794,0.0008292542,0.000942755],"domain_scores_gemma":[0.9981661,0.000608491,0.00007879834,0.0004777691,0.0002944697,0.0003743025],"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":[0.0001006945,0.00002958365,0.00002876654,0.00154698,0.0001556632,0.000009285464,0.0001953743,0.01619453,0.9661494,0.00001186851,0.001183752,0.01439408],"study_design_scores_gemma":[0.0006099431,0.00006419441,0.00005230449,0.0005900874,0.00007000197,0.00004843341,0.0001716795,0.0869516,0.9075404,0.00001593929,0.0034688,0.0004166185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938528,0.00216544,0.0008526496,0.0004602998,0.000901015,0.001208614,0.00008781879,0.0003491507,0.0001222229],"genre_scores_gemma":[0.997257,0.000485991,0.00006198729,0.000006917079,0.001740626,0.0002051349,0.00004918659,0.0001507774,0.00004238472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07075707,"threshold_uncertainty_score":0.9998084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07872483576878958,"score_gpt":0.3206496519889203,"score_spread":0.2419248162201307,"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."}}