{"id":"W4288925160","doi":"10.1101/2022.07.29.502078","title":"Solid-state enzymatic hydrolysis of mixed PET-cotton textiles","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; European Commission; Centre in Green Chemistry and Catalysis","keywords":"Depolymerization; Polyester; Polyethylene terephthalate; Cellulose; Raw material; Enzymatic hydrolysis; Terephthalic acid; Yield (engineering); Materials science; Hydrolysis; Cutinase; Polymer; Cellulase; Waste management; Chemistry; Organic chemistry; Pulp and paper industry; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001703972,0.0004994893,0.0002193471,0.00016945,0.00008634332,0.0002761292,0.0001306761,0.0002102358,0.001024264],"category_scores_gemma":[0.0001469913,0.0001357021,0.0002683746,0.0002252988,0.0001245016,0.0002269708,0.0001757419,0.0002813945,0.0003864267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001759401,"about_ca_system_score_gemma":0.0001167378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002907923,"about_ca_topic_score_gemma":0.0005606779,"domain_scores_codex":[0.9998343,0.00002765588,0.00001573226,0.00004325635,0.00004069531,0.00003839895],"domain_scores_gemma":[0.9999187,0.00002078871,0.00002204618,0.00001128592,0.00001172206,0.00001553973],"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":[0.0000423613,0.0000105868,0.0000748129,0.00002655733,0.000004352537,0.0000501764,0.0000082325,0.00007986982,0.9989111,0.00002102058,0.000007911661,0.0007629297],"study_design_scores_gemma":[0.000002793445,0.00007033299,0.0005568946,0.000002719003,0.000004132887,0.00005364314,0.000007079729,0.0004596956,0.9985282,0.000007564162,0.0003051035,0.000001800291],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920845,0.0006539263,0.00549715,0.00002162868,0.00001591277,0.00002282508,0.0001846751,0.00004335468,0.001475996],"genre_scores_gemma":[0.9954389,0.0003219337,0.002293951,0.000008413207,0.000003785745,0.00001076272,0.0002047145,0.00001855316,0.001699012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001024264,"threshold_uncertainty_score":0.003426552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007829113841426871,"score_gpt":0.2072479937026627,"score_spread":0.1994188798612358,"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."}}