{"id":"W4416080971","doi":"10.1007/978-3-032-00167-2_30","title":"Use of the CESL Process for PGM Recovery","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nickel Institute","funders":"","keywords":"Process (computing); Product (mathematics); Work (physics); Group (periodic table); Order (exchange); Refining (metallurgy)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000400265,0.0009399739,0.0005358426,0.001607174,0.0007663947,0.002084475,0.001034068,0.0009826785,0.02679332],"category_scores_gemma":[0.0004575455,0.0004767606,0.0006186836,0.001629418,0.0007032335,0.002447054,0.001369882,0.001723707,0.02413727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062445,"about_ca_system_score_gemma":0.0007433993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001170202,"about_ca_topic_score_gemma":0.00280156,"domain_scores_codex":[0.999637,0.00003018974,0.00001184341,0.00005883224,0.0002373738,0.00002476468],"domain_scores_gemma":[0.9998719,0.00004567428,0.000007586768,0.0000289441,0.00004031329,0.000005585528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006542153,0.000132772,0.0001528951,0.001280088,0.00001886362,0.0006775612,0.0003775032,0.002608035,0.1361575,0.1657642,0.06096561,0.6317996],"study_design_scores_gemma":[0.000003649662,0.00002605744,0.000160308,0.0001272293,0.00001127606,0.0006061391,0.00005295205,0.001862294,0.1471301,0.01185088,0.8381498,0.00001926083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00541984,0.01509374,0.2618416,0.001221055,0.001067432,0.0002121749,0.0008008235,0.002104969,0.7122384],"genre_scores_gemma":[0.02916622,0.0173584,0.09079033,0.0008519155,0.000177544,0.0001362644,0.001071271,0.001222876,0.8592252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02679332,"threshold_uncertainty_score":0.08963263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04696086712127379,"score_gpt":0.2658435060982788,"score_spread":0.218882638977005,"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."}}