{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002638648,0.0001203845,0.0001914481,0.00003781691,0.0000232786,0.000009259591,0.00009650621,0.00005298385,0.000001194166],"category_scores_gemma":[0.0003983044,0.00008640067,0.000009990857,0.0001254957,0.0002985064,0.00004142191,0.00005902791,0.0001510341,3.06341e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003848088,"about_ca_system_score_gemma":0.000006193025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004070076,"about_ca_topic_score_gemma":5.03623e-7,"domain_scores_codex":[0.9995135,0.00001181694,0.0001434802,0.0001612197,0.00006096956,0.0001090154],"domain_scores_gemma":[0.9997607,0.00002536499,0.00004573949,0.00008947353,0.00005650169,0.00002217346],"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":[0.0003572864,0.00004356484,0.02223731,0.004487962,0.0002837091,0.000005987401,0.004031103,0.0542132,0.8911265,0.0003424714,0.0001246654,0.02274631],"study_design_scores_gemma":[0.0001196744,0.0002317171,0.07516933,0.0001031157,0.000008431654,0.000005048666,0.00008295656,0.0009875648,0.9230508,0.0001154026,0.00002015562,0.0001057875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999095,0.0002933611,0.00002877837,0.00008595,0.00000959816,0.0001534946,0.000007900104,0.0002940227,0.0000318937],"genre_scores_gemma":[0.9987181,0.00003095895,0.001208655,0.00001558145,0.000005414114,0.000007844958,0.000001276579,0.000009373035,0.000002776008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05322564,"threshold_uncertainty_score":0.3523317,"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."}}