{"id":"W4406221831","doi":"10.1021/acs.langmuir.4c04262","title":"Large Language Modeling to Assist Natural Polyphenols as Green Precipitants for Recycling Spent Batteries","year":2025,"lang":"en","type":"article","venue":"Langmuir","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Young Scientists Fund; State Key Laboratory of Polymer Materials Engineering; Sichuan University; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Environmental pollution; Sustainability; Battery (electricity); Environmental science; Computer science; Polyphenol; Biochemical engineering; Process engineering; Waste management; Chemistry; Engineering; Power (physics); Ecology; Environmental protection","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":[],"consensus_categories":[],"category_scores_codex":[0.00007538198,0.0001053491,0.0001249497,0.00008975346,0.00009792467,0.00006455653,0.00009975602,0.00004992278,0.0000751231],"category_scores_gemma":[0.00004725224,0.0001030645,0.00004119636,0.000142502,0.000003820847,0.0001126709,0.00002138543,0.00008927623,0.00007338067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003941376,"about_ca_system_score_gemma":0.00001222222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002708384,"about_ca_topic_score_gemma":0.000244188,"domain_scores_codex":[0.9994096,0.000007375072,0.0001669024,0.0001387194,0.00008059909,0.0001968717],"domain_scores_gemma":[0.9997633,0.00003702258,0.00001446449,0.0001080371,0.00003982678,0.00003735867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001057829,0.0005357777,0.004269633,0.005341617,0.001339315,0.00007458611,0.07169698,0.1301072,0.4496896,0.06432384,0.1275104,0.1440532],"study_design_scores_gemma":[0.002010315,0.00008274226,0.00298567,0.000432798,0.00009990203,0.00001333999,0.009162401,0.5254565,0.08535071,0.003014792,0.3702175,0.001173335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8340876,0.001400563,0.1277539,0.001709764,0.001535506,0.00049722,0.00008304045,0.000743506,0.03218893],"genre_scores_gemma":[0.9846513,0.000007675014,0.001093248,0.0006504732,0.0001314179,0.00005671143,0.0000408307,0.00001912817,0.01334922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3953492,"threshold_uncertainty_score":0.4202848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01303837331893314,"score_gpt":0.3044976158902586,"score_spread":0.2914592425713254,"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."}}