{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002861935,0.0004932927,0.0004363656,0.000511467,0.0004314244,0.0005022862,0.001242468,0.0006213901,0.01197306],"category_scores_gemma":[0.0004247532,0.0005193158,0.0002389779,0.0003236472,0.0004025713,0.0006236358,0.0006458736,0.0007963121,0.007589255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624857,"about_ca_system_score_gemma":0.0003249547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003805052,"about_ca_topic_score_gemma":0.0005440377,"domain_scores_codex":[0.9997285,0.00002055558,0.00003516955,0.00009885344,0.00009183476,0.0000250528],"domain_scores_gemma":[0.9997678,0.00005531434,0.00002381714,0.00008477896,0.00003670252,0.00003149259],"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":[0.0001130154,0.00003201221,0.0002562243,0.0001826367,0.000007351085,0.0001428906,0.00004234604,0.0001414749,0.9627494,0.001054609,0.006666776,0.02861128],"study_design_scores_gemma":[0.00003871063,0.0001487308,0.002104319,0.00002816992,0.00001620044,0.0006040026,0.00002255096,0.004127798,0.9132854,0.0003881072,0.0791985,0.00003740014],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08910065,0.001762179,0.8577074,0.001153605,0.001238175,0.001334846,0.004858865,0.02766398,0.01518028],"genre_scores_gemma":[0.1794359,0.001101022,0.7779614,0.0009309313,0.000281105,0.001817969,0.005300557,0.002064292,0.03110681],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01197306,"threshold_uncertainty_score":0.04005384,"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."}}