{"id":"W3206442363","doi":"10.3934/math.2022059","title":"Application of transport-based metric for continuous interpolation between cryo-EM density maps","year":2021,"lang":"en","type":"article","venue":"AIMS Mathematics","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Morphing; Computer science; Regularization (linguistics); Interpolation (computer graphics); Set (abstract data type); Cryo-electron microscopy; Key (lock); Metric (unit); Algorithm; Statistical physics; Physics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.000103748,0.00009363936,0.000172316,0.00003209891,0.00004237223,0.000005265347,0.00009780722,0.00009881507,0.000002961019],"category_scores_gemma":[0.00004078601,0.00009825139,0.00009370643,0.0001539225,0.00002725151,0.000002603755,0.00001550354,0.00004270233,0.000001168683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001079041,"about_ca_system_score_gemma":0.0000424084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002237781,"about_ca_topic_score_gemma":0.00001044599,"domain_scores_codex":[0.9993539,0.000008744871,0.0002642621,0.0001897349,0.00006402045,0.0001193662],"domain_scores_gemma":[0.9992554,0.00003520109,0.0001703616,0.0003175684,0.0001928019,0.00002872111],"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":[0.00001011264,0.0001161074,0.001194721,0.0001211798,0.00002597748,1.085018e-7,0.00003199974,0.00003359498,0.9916975,0.002488753,0.0002585517,0.004021353],"study_design_scores_gemma":[0.000227526,0.00009814368,0.0003163129,0.00001433674,0.00005513526,0.000001731896,0.0000537978,0.0008078601,0.9859084,0.006821241,0.005587026,0.0001085224],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1627858,0.0000981266,0.8365625,0.00005305063,0.000007297898,0.0003350869,0.00007462753,0.00001875592,0.0000647248],"genre_scores_gemma":[0.778951,0.00001469543,0.2200496,0.00004701502,0.00003878473,0.00008917547,0.0006981351,0.00001715475,0.00009433625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6165129,"threshold_uncertainty_score":0.4006575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009178936059451304,"score_gpt":0.3031824313779357,"score_spread":0.2940034953184844,"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."}}