{"id":"W4377090625","doi":"10.1016/j.matlet.2023.134503","title":"Corrigendum to “Assessing roughness of extrusion printed soft materials using a semi-quantitative method” [Mater. Lett. 303 (2021) 130480]","year":2023,"lang":"en","type":"erratum","venue":"Materials Letters","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Materials science; Extrusion; Composite material; Surface finish; Soft materials; Engineering drawing; Polymer science; Nanotechnology; Engineering","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":[0.00114416,0.001125855,0.002004063,0.0009587162,0.0002238763,0.0005428029,0.001004441,0.0009181156,0.0006588321],"category_scores_gemma":[0.0005147162,0.001127153,0.0001975146,0.0005471048,0.0001773692,0.0002846853,0.0009923737,0.0006481431,0.0002327307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003737824,"about_ca_system_score_gemma":0.00007432655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004063605,"about_ca_topic_score_gemma":0.000006160012,"domain_scores_codex":[0.9953786,0.000428073,0.001470925,0.001026223,0.0005842453,0.001112002],"domain_scores_gemma":[0.9977639,0.0002140387,0.000709748,0.001044154,0.0001491521,0.0001189622],"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":[0.00003802018,0.000014635,0.000002616918,0.001798169,0.0002390972,0.00009101722,0.0003734033,0.00262207,0.8340553,0.00003352053,0.1598028,0.0009293645],"study_design_scores_gemma":[0.0002169393,0.0000437976,0.0003016041,0.003329895,0.0001556441,0.00001883118,0.0002817005,0.00048983,0.975299,0.0001316621,0.01856401,0.001167061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.762218,0.00008348586,0.1352415,0.0004324084,0.09605733,0.00110672,0.001644973,0.002951978,0.0002636242],"genre_scores_gemma":[0.5423588,0.0007854472,0.40163,0.00196589,0.01226667,0.001424894,0.007543517,0.005406522,0.02661823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2663885,"threshold_uncertainty_score":0.9991179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04814769532769533,"score_gpt":0.3051100051491675,"score_spread":0.2569623098214722,"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."}}