{"id":"W3041905801","doi":"10.3390/foods9070907","title":"Influence of Selected Product and Process Parameters on Microstructure, Rheological, and Textural Properties of 3D Printed Cookies","year":2020,"lang":"en","type":"article","venue":"Foods","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rheology; Ingredient; 3d printed; 3D printing; Microstructure; Materials science; Porosity; Food science; Composite material; Raw material; Chemistry","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":[0.0003070254,0.0004162704,0.0002093217,0.0003353671,0.000115718,0.0004534161,0.000161549,0.0002646695,0.00103106],"category_scores_gemma":[0.0007989556,0.0002420131,0.0003302201,0.0003146011,0.0001697346,0.000353695,0.0002074562,0.000477799,0.0002692146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001340786,"about_ca_system_score_gemma":0.0001453105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006153182,"about_ca_topic_score_gemma":0.001570443,"domain_scores_codex":[0.9998319,0.00001931457,0.00002225533,0.00004679642,0.00005975725,0.00001996245],"domain_scores_gemma":[0.9996997,0.0001153814,0.00006820506,0.00002541397,0.0000686619,0.00002271431],"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":[0.0001494816,0.00004206044,0.001027598,0.0002116252,0.00002226845,0.0001445395,0.00006607312,0.001020351,0.9916463,0.00005246386,0.0000595219,0.00555766],"study_design_scores_gemma":[0.00000902704,0.0003273696,0.01301598,0.00001641677,0.00005866318,0.0001461624,0.0000610142,0.002708768,0.9818823,0.00002710317,0.001721434,0.00002576518],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930983,0.001002308,0.004297791,0.00002977079,0.00002412759,0.00003426208,0.0002991584,0.000101986,0.001112294],"genre_scores_gemma":[0.9840432,0.001362169,0.01256667,0.00005226861,0.000007494286,0.00007389369,0.0004015282,0.00009913316,0.001393574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00103106,"threshold_uncertainty_score":0.003449261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01787949697780645,"score_gpt":0.2092896563563544,"score_spread":0.1914101593785479,"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."}}