{"id":"W2888347975","doi":"10.1101/400317","title":"ENRICH: a fast method to improve the quality of flexible macromolecular reconstructions","year":2018,"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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agencia Estatal de Investigación; Comunidad de Madrid; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Single particle analysis; Flexibility (engineering); Resolution (logic); Homogeneous; Cryo-electron microscopy; Projection (relational algebra); Computer science; Particle (ecology); Macromolecule; Quality (philosophy); Algorithm; Contrast transfer function; Biological system; Artificial intelligence; Statistical physics; Optics; Physics; Chemistry; Mathematics; Geology; Spherical aberration; Statistics","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":[0.0007042346,0.0003667391,0.0003738085,0.0000884884,0.0001862041,0.00005423035,0.0007053575,0.0004110982,0.00002595203],"category_scores_gemma":[0.0001101632,0.0003395603,0.0002119492,0.0003532245,0.0002022285,0.000004804494,0.000682801,0.0003741816,0.00001333569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006706322,"about_ca_system_score_gemma":0.0003523347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006442291,"about_ca_topic_score_gemma":0.000003112127,"domain_scores_codex":[0.9977593,0.000172745,0.0005524807,0.0009258714,0.0001736936,0.0004158442],"domain_scores_gemma":[0.9968929,0.0000230801,0.0004563751,0.001908883,0.0005695067,0.0001493076],"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":[0.00002990457,0.00006049218,0.0001596555,0.00005622064,0.0001113485,4.147768e-7,0.000003869195,0.0000262039,0.9975076,0.001322107,0.0006926488,0.00002957977],"study_design_scores_gemma":[0.000122812,0.000128823,0.001435798,0.00003731412,0.0000510161,4.151795e-8,0.000003708464,0.00001091515,0.9750779,0.00003056492,0.02272406,0.0003770453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3237102,0.0005615158,0.6735374,0.000232073,0.000271191,0.001024035,0.0005297844,0.0001022912,0.0000315569],"genre_scores_gemma":[0.710735,0.0001471262,0.2873217,0.0003370631,0.0004292506,0.0008763918,0.000002144562,0.0001003498,0.00005096326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3870248,"threshold_uncertainty_score":0.9999056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405474349453162,"score_gpt":0.3248025661482493,"score_spread":0.3107478226537176,"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."}}