{"id":"W2944106437","doi":"10.1101/632125","title":"<i>Shake-it-off</i> : A simple ultrasonic cryo-EM specimen preparation device","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; Hospital for Sick Children; University of Toronto; Canada Research Chairs","keywords":"Materials science; Sample (material); Ultrasonic sensor; Computer science; Microscope; Grid; Sample preparation; Cryo-electron microscopy; Interface (matter); Vitrification; Nanotechnology; Optics; Acoustics; Composite material; Chromatography; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002797143,0.0005743945,0.0004280759,0.00008591523,0.0001679198,0.0001578745,0.0006895246,0.0007087094,0.00004573811],"category_scores_gemma":[0.00007242132,0.0006599422,0.0001989279,0.0002522417,0.00007451286,0.0000154881,0.0004765294,0.0005450457,0.0001010424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001753641,"about_ca_system_score_gemma":0.0006017277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001458427,"about_ca_topic_score_gemma":0.00001355904,"domain_scores_codex":[0.9972185,0.0000751199,0.0005158965,0.001331467,0.0002289569,0.0006300818],"domain_scores_gemma":[0.9971113,0.00001990028,0.0004428841,0.001886223,0.0003619005,0.0001778178],"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":[0.00004466633,0.00009947378,0.0005027525,0.00009889631,0.00008180211,0.000001769822,0.000003990869,0.0002870606,0.9909829,0.0001623099,0.007728482,0.000005852348],"study_design_scores_gemma":[0.0002296044,0.000125676,0.001178484,0.00006548844,0.00005690854,3.616676e-8,0.000002126785,0.00009694208,0.7549664,0.000006551339,0.2426735,0.0005983594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9444894,0.003754787,0.04838403,0.0002337054,0.0003010976,0.001939843,0.0004767209,0.0003270683,0.00009339357],"genre_scores_gemma":[0.9842135,0.001858725,0.01174063,0.0007680462,0.0005910282,0.000556696,0.00002043892,0.0001631125,0.00008776467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2360166,"threshold_uncertainty_score":0.9995852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009266167184986476,"score_gpt":0.2811861995012962,"score_spread":0.2719200323163097,"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."}}