{"id":"W6945138878","doi":"10.25500/edata.bham.00001247","title":"Research data supporting the publication \"Pre-Treatment and Valorisation of Critical Materials from Lithium-Ion Batteries Using Electrostatic and Magnetic Separation\"","year":2025,"lang":"en","type":"dataset","venue":"University of Birmingham Research Portal (University of Birmingham)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Faraday Institution","keywords":"Separator (oil production); Research data; Research development; Magnetic separation; Valorisation; Magnet","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":[0.002102881,0.00286133,0.00306609,0.006265489,0.001203002,0.003356992,0.002809795,0.003413561,0.149327],"category_scores_gemma":[0.01390592,0.0006594304,0.00207837,0.01205142,0.0007296309,0.00169523,0.002374937,0.00178753,0.09964764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001900502,"about_ca_system_score_gemma":0.0056457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01597028,"about_ca_topic_score_gemma":0.03704799,"domain_scores_codex":[0.9974024,0.0003699926,0.0004491812,0.0007968073,0.0006750464,0.0003065776],"domain_scores_gemma":[0.9922473,0.004067389,0.0007594326,0.0008040364,0.00170335,0.0004185395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000298357,0.00005815546,0.001811708,0.01629066,0.0002386979,0.0001118807,0.00007644223,0.001215041,0.0008090581,0.001547324,0.9675802,0.009962424],"study_design_scores_gemma":[0.0002429255,0.00003402883,0.002289761,0.001709283,0.0001827798,0.00006392856,0.00005331558,0.0002893764,0.0006456746,0.001884227,0.9925654,0.00003917826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008414267,0.0003367924,0.00006718484,0.0000621555,0.0000276026,0.00000895721,0.9986542,0.0001209989,0.0006379012],"genre_scores_gemma":[0.0004223932,0.0004299202,0.0004513199,0.00009629838,0.00001080668,0.00007371422,0.9977864,0.00007275627,0.0006564778],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.149327,"threshold_uncertainty_score":0.4995486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09973964302480534,"score_gpt":0.3989447336432113,"score_spread":0.2992050906184059,"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."}}