{"id":"W2052407039","doi":"10.1016/j.bpj.2013.11.3545","title":"Going Backward: An Efficient Multiscale Approach using Reverse Transformation","year":2014,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Representation (politics); Projection (relational algebra); Molecular dynamics; Relaxation (psychology); Transformation (genetics); Biological system; Statistical physics; Simple (philosophy); Membrane; Force field (fiction); Algorithm; Scale (ratio); Chemical physics; Computer science; Chemistry; Physics; Computational chemistry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004855708,0.0007911518,0.0009102117,0.0007098754,0.0004969574,0.001047166,0.001500346,0.0008733697,0.008096919],"category_scores_gemma":[0.001562259,0.0005657991,0.001201019,0.0006259343,0.0004915126,0.001417345,0.00216768,0.001512142,0.002839727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002249511,"about_ca_system_score_gemma":0.0006507548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00143595,"about_ca_topic_score_gemma":0.002388721,"domain_scores_codex":[0.9996661,0.00005197418,0.00001990136,0.00006518016,0.0001627113,0.00003421297],"domain_scores_gemma":[0.9995684,0.0001400521,0.00002881398,0.0001430991,0.0000892393,0.00003037825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003172138,0.0002629835,0.0006317291,0.000339844,0.0001331205,0.0004574861,0.0003347448,0.08743724,0.155055,0.1850491,0.008218799,0.5617626],"study_design_scores_gemma":[0.00002270856,0.0000637042,0.0001364963,0.00001068814,0.00003251443,0.0001557592,0.0000368845,0.9368817,0.01641093,0.03870462,0.007519659,0.00002429528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005633237,0.00003713025,0.9918868,0.00006006259,0.00004150685,0.00002610721,0.00004222289,0.0007333388,0.001539552],"genre_scores_gemma":[0.09012658,0.0001097462,0.90422,0.00007483446,0.00003492007,0.00007913318,0.0002192629,0.0007049341,0.004430625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008096919,"threshold_uncertainty_score":0.02708691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109340876384285,"score_gpt":0.3072223592864224,"score_spread":0.2962882716479939,"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."}}