{"id":"W2587066259","doi":"10.1039/c6sm02674e","title":"Effect of internal architecture on microgel deformation in microfluidic constrictions","year":2017,"lang":"en","type":"article","venue":"Soft Matter","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Division of Materials Research; Materials Research Science and Engineering Center, Harvard University; National Science Foundation","keywords":"Microfluidics; Deformation (meteorology); Materials science; Soft lithography; Nanotechnology; Particle (ecology); Internal flow; Finite element method; Work (physics); Soft matter; Buckling; Mechanics; Flow (mathematics); Composite material; Mechanical engineering; Structural engineering; Physics; Fabrication; Engineering; Chemical 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":[],"consensus_categories":[],"category_scores_codex":[0.0002590235,0.0000754585,0.0001056622,0.0001509205,0.00004141849,0.00003452996,0.0002282893,0.00006151078,0.0001396406],"category_scores_gemma":[0.0001222565,0.00006155021,0.0000340074,0.00004603458,0.00009525265,0.00004566253,0.00005748327,0.0002705922,0.000373285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000415241,"about_ca_system_score_gemma":0.00000729913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004062703,"about_ca_topic_score_gemma":0.000002923,"domain_scores_codex":[0.9994591,0.00003203841,0.0001453896,0.00007468059,0.000125927,0.0001629036],"domain_scores_gemma":[0.9995277,0.0001529521,0.00002808183,0.0002412402,0.00001303118,0.00003701037],"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":[0.00008274344,0.00004008797,0.3208924,0.0009433976,0.00005983619,0.00001538692,0.0003325382,0.0001814769,0.5071329,0.00002397531,0.02307001,0.1472253],"study_design_scores_gemma":[0.001024638,0.0001452879,0.116347,0.0004954051,0.000008801788,0.00002787802,0.000006479188,0.002000485,0.8738974,0.0001922264,0.005680455,0.000173847],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916732,0.00006447363,0.003436257,0.0002182438,0.0002076257,0.000114976,0.000007149828,0.00003370289,0.004244338],"genre_scores_gemma":[0.9997476,0.00001102756,0.00006960149,0.00005576345,0.00003360966,0.00001062398,0.000002492006,0.00001182135,0.00005751273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3667645,"threshold_uncertainty_score":0.4797948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006236521518794769,"score_gpt":0.2664963682111701,"score_spread":0.2602598466923753,"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."}}