{"id":"W2327174080","doi":"10.1021/acs.jctc.5b00209","title":"Computational Lipidomics with <i>insane</i>: A Versatile Tool for Generating Custom Membranes for Molecular Simulations","year":2015,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1212,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Deutsche Forschungsgemeinschaft; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Lipidomics; Computer science; Nanotechnology; Membrane; Data science; Computational biology; Chemistry; Materials science; Bioinformatics; Biology; Biochemistry","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":[0.0004127745,0.0000931571,0.0001689216,0.00003726551,0.00005372604,0.00002878141,0.00005087632,0.00005274866,7.387267e-7],"category_scores_gemma":[0.0002863702,0.00007572843,0.00006773764,0.00004619723,0.00004724719,0.00001028292,0.00001935176,0.0000449881,6.884262e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001052583,"about_ca_system_score_gemma":0.00007138577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.388812e-7,"about_ca_topic_score_gemma":1.105021e-7,"domain_scores_codex":[0.9994097,0.00004119665,0.00023579,0.0001174411,0.0001003256,0.0000954988],"domain_scores_gemma":[0.9990945,0.0001926164,0.0001978868,0.00004144272,0.0004170983,0.00005646962],"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":[0.002816215,0.00009606726,0.00008248352,0.00008331844,0.0003212439,0.000001091919,0.000210963,0.2979765,0.6841395,0.010106,0.0005559726,0.003610639],"study_design_scores_gemma":[0.01282011,0.002444691,0.00003039507,0.00005926233,0.0004515868,0.0001817749,0.0005912594,0.1756036,0.635295,0.1619362,0.009974737,0.0006113463],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5749955,0.0004027793,0.4243378,0.00007186097,0.00004972383,0.0001119674,0.0000142317,0.000001895178,0.00001426495],"genre_scores_gemma":[0.9200404,0.00001323458,0.07938606,0.0001893064,0.0002700422,0.000007337742,0.00007277284,0.00001121918,0.000009673015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3450449,"threshold_uncertainty_score":0.3088116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331562127467239,"score_gpt":0.2714022487137325,"score_spread":0.2580866274390601,"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."}}