{"id":"W2566390043","doi":"10.1109/tase.2016.2613684","title":"Microscale Compression and Shear Testing of Soft Materials Using an MEMS Microgripper With Two-Axis Actuators and Force Sensors","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Microscale chemistry; Actuator; Microelectromechanical systems; Materials science; Capacitive sensing; Polydimethylsiloxane; Shear force; Compression (physics); Shear (geology); Mechanical compression; Mechanical engineering; Acoustics; Composite material; Nanotechnology; Engineering; Biomedical engineering; Electrical engineering; Physics","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":[0.0001536632,0.00009848175,0.0001064006,0.0001093904,0.0002186753,0.00007020179,0.00004870012,0.00001963356,0.000009854191],"category_scores_gemma":[0.000001601792,0.00007056386,0.000008473767,0.0001965121,0.0001730209,0.0003974929,0.000003618739,0.00003641378,3.162209e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001595979,"about_ca_system_score_gemma":0.00002140782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007722258,"about_ca_topic_score_gemma":5.6563e-7,"domain_scores_codex":[0.9994186,0.000005150339,0.0001381295,0.0002038833,0.00009638024,0.0001378456],"domain_scores_gemma":[0.9996719,0.00003760814,0.00005135907,0.0001096018,0.0000612246,0.00006826118],"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":[0.000002871751,0.00001466831,0.0001197629,0.00001178016,0.000003165359,3.709602e-8,0.0001301348,0.00229017,0.9732502,0.00007112767,4.33863e-7,0.02410571],"study_design_scores_gemma":[0.0001987865,0.00005745251,0.000499535,0.0001457801,0.00001165488,0.000007230413,0.00009925746,0.05594034,0.9428788,0.00003485945,0.00001733903,0.0001088963],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5725657,0.000002198846,0.4272664,0.00001185794,0.00001475529,0.00007921037,0.00001747296,0.0000348575,0.000007512753],"genre_scores_gemma":[0.9718848,0.000002024831,0.02806687,0.000004903648,0.00001021105,0.00001133013,5.2749e-7,0.000009931267,0.000009378919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3993191,"threshold_uncertainty_score":0.2877511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346745832796995,"score_gpt":0.2608717179586335,"score_spread":0.2474042596306635,"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."}}