{"id":"W4283314070","doi":"10.3389/fmolb.2022.903148","title":"Approaches to Using the Chameleon: Robust, Automated, Fast-Plunge cryoEM Specimen Preparation","year":2022,"lang":"en","type":"article","venue":"Frontiers in Molecular Biosciences","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Canadian Institute for Advanced Research; Engineering and Physical Sciences Research Council; Howard Hughes Medical Institute; National Institutes of Health; National Science Foundation","keywords":"Workflow; Computer science; Key (lock); Process (computing); Field (mathematics); Sample (material); Nanotechnology; Materials science; Chemistry; Database; Mathematics; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009240666,0.001226499,0.0007528641,0.002055159,0.002492589,0.003982794,0.00351562,0.002336078,0.008700257],"category_scores_gemma":[0.01261896,0.001518681,0.0008319042,0.001197477,0.003033183,0.005673353,0.005454539,0.003099093,0.006752549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168402,"about_ca_system_score_gemma":0.002106075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001781716,"about_ca_topic_score_gemma":0.004117974,"domain_scores_codex":[0.9959227,0.0008477225,0.0002102926,0.0008838616,0.001847772,0.0002876942],"domain_scores_gemma":[0.9935145,0.001891288,0.0005079897,0.002474037,0.001194431,0.0004177749],"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":[0.0007615442,0.0002905963,0.003342917,0.001836537,0.000133474,0.001834157,0.00415139,0.004418595,0.5384188,0.03812578,0.0299873,0.3766989],"study_design_scores_gemma":[0.00008798028,0.0007258343,0.005358779,0.0005307242,0.00008303997,0.007980927,0.00120952,0.01551254,0.5166273,0.01993616,0.4313838,0.0005634114],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02690508,0.003381704,0.9457897,0.002556969,0.000500175,0.000975606,0.0004084967,0.007966382,0.01151593],"genre_scores_gemma":[0.05371909,0.00258367,0.9328347,0.0006887647,0.0001177608,0.0006468548,0.0007972031,0.001612479,0.006999546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009240666,"threshold_uncertainty_score":0.04886991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289079569954798,"score_gpt":0.2995245324146875,"score_spread":0.2706165754192076,"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."}}