{"id":"W3111754743","doi":"10.1093/bioinformatics/btaa1019","title":"<i>MorphOT</i> : transport-based interpolation between EM maps with UCSF <i>ChimeraX</i>","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Morphing; Computer science; Interpolation (computer graphics); Documentation; Code (set theory); Source code; Software; Metric (unit); Algorithm; Linear interpolation; Computer graphics (images); Data mining; Computational science; Theoretical computer science; Image (mathematics); Artificial intelligence; Programming language; Pattern recognition (psychology); Set (abstract data type)","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":[0.002925488,0.002656149,0.002625331,0.001558554,0.00247669,0.003894737,0.007759442,0.003040628,0.0743667],"category_scores_gemma":[0.009785608,0.002141725,0.002209504,0.002169461,0.001278653,0.00343452,0.005067665,0.005378353,0.03564931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001887836,"about_ca_system_score_gemma":0.003208229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0119684,"about_ca_topic_score_gemma":0.01529504,"domain_scores_codex":[0.9987971,0.0002106199,0.00009313788,0.0003577116,0.0004192474,0.0001222443],"domain_scores_gemma":[0.9979582,0.0007134816,0.0002123229,0.0005798711,0.0002931338,0.0002430297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006452234,0.000198664,0.003351688,0.003190824,0.0005171339,0.000594098,0.0007874311,0.03285947,0.01904493,0.02563715,0.82623,0.0869434],"study_design_scores_gemma":[0.0005282543,0.0001336758,0.002799843,0.0007726044,0.0001945715,0.00128139,0.0002588737,0.4760961,0.05964065,0.04437883,0.4135093,0.0004057677],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01499887,0.001071404,0.5226716,0.002434795,0.0008227613,0.0002723682,0.06308262,0.3835162,0.01112948],"genre_scores_gemma":[0.06069382,0.00124445,0.6301143,0.001088231,0.0001430106,0.0009807622,0.1068775,0.1916527,0.007205273],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0743667,"threshold_uncertainty_score":0.2487814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009067360275982882,"score_gpt":0.2572769591131348,"score_spread":0.2482095988371519,"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."}}