{"id":"W3205573716","doi":"10.1021/acssuschemeng.1c05587","title":"3D-Printed Thermoset Biocomposites Based on Forest Residues by Delayed Extrusion of Cold Masterbatch (DECMA)","year":2021,"lang":"en","type":"article","venue":"ACS Sustainable Chemistry & Engineering","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Academy of Finland","keywords":"Masterbatch; Extrusion; Materials science; Thermosetting polymer; Biocomposite; Composite material; 3D printing; Curing (chemistry); Machinability; Composite number; Machining; Nanocomposite","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":[0.000114063,0.0003383299,0.0003114588,0.00007734678,0.00007239403,0.00005391741,0.0003274245,0.0002218555,0.00008351865],"category_scores_gemma":[0.000228862,0.0003708998,0.00008070319,0.0002674301,0.00004621326,0.00008098926,0.0001892642,0.0003738466,0.000003743464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717799,"about_ca_system_score_gemma":0.00003641424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000149531,"about_ca_topic_score_gemma":5.4484e-7,"domain_scores_codex":[0.9986016,0.00001005856,0.0003032,0.0003301103,0.0002089446,0.0005460604],"domain_scores_gemma":[0.9989493,0.0001846302,0.00005822547,0.0005874282,0.0001481016,0.00007227783],"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":[0.00001252116,0.00004032808,0.000247411,0.0009252261,0.00006020138,0.00008767952,0.00004152938,0.230085,0.7670617,0.0001390917,0.0009735876,0.0003257317],"study_design_scores_gemma":[0.0003093602,0.00002649863,0.000349343,0.0001889452,0.00001953542,0.000005098873,0.0003055872,0.0344829,0.955277,0.00005496117,0.008637683,0.0003430448],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819462,0.0005331158,0.01323061,0.00008460927,0.00005304517,0.0001397327,0.00004055423,0.001197617,0.00277453],"genre_scores_gemma":[0.9954731,0.00003899802,0.00273846,0.00001078054,0.0000319716,0.00004156995,0.0001079869,0.00008195877,0.001475126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1956021,"threshold_uncertainty_score":0.9998743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004122505551775536,"score_gpt":0.1820007703109206,"score_spread":0.1778782647591451,"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."}}