{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001511248,0.0002731453,0.0001825174,0.0002781415,0.0001613434,0.0003151916,0.0001824016,0.0003075234,0.003525349],"category_scores_gemma":[0.0001640753,0.0002013525,0.0002247629,0.0002078563,0.0001351525,0.0003016879,0.0001432555,0.0004683564,0.001199974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014119,"about_ca_system_score_gemma":0.0001110871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001671689,"about_ca_topic_score_gemma":0.0009315667,"domain_scores_codex":[0.9999113,0.000004368494,0.000006761076,0.0000259463,0.00003459521,0.00001695307],"domain_scores_gemma":[0.9999193,0.00002026942,0.00002135643,0.00001606805,0.00001313256,0.000009858105],"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":[0.00004150792,0.00001064712,0.00005270167,0.00004239405,0.000002250645,0.00007622244,0.00002254692,0.0001796979,0.9950826,0.0001293463,0.0001130383,0.004247092],"study_design_scores_gemma":[0.000003774966,0.0000532972,0.0007830428,0.000006041521,0.000003996326,0.00009904787,0.000007146451,0.0005623653,0.9946021,0.0000354618,0.00383831,0.000005335256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9560338,0.001895209,0.02721797,0.00008528949,0.0002441278,0.00006159034,0.0008709942,0.0008594995,0.01273147],"genre_scores_gemma":[0.9545123,0.0008923403,0.02902191,0.00006479552,0.00001963442,0.00007568808,0.0006072099,0.0002537614,0.01455225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003525349,"threshold_uncertainty_score":0.01179343,"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."}}