{"id":"W4386965784","doi":"10.26434/chemrxiv-2023-lh7bq-v2","title":"Martini 3 Coarse-Grained Force Field for cholesterol","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Lipid Membrane Structure and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Lawrence Livermore National Laboratory; Alliance de recherche numérique du Canada; Centre National de la Recherche Scientifique; National Supercomputing Center, Korea Institute of Science and Technology Information; Grand Équipement National De Calcul Intensif; Max-Planck-Gesellschaft; Canada Research Chairs; Laboratory Directed Research and Development; Fundação para a Ciência e a Tecnologia; U.S. Department of Energy","keywords":"Rigidity (electromagnetism); Force balance; Cholesterol; Permeability (electromagnetism); Force field (fiction); Nanotechnology; Lipid bilayer; Chemistry; Computer science; Biophysics; Materials science; Membrane; Mechanics; Composite material; Physics; Biochemistry; Artificial intelligence; Biology","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":[0.000455082,0.0006538988,0.0009281235,0.0005372487,0.0009639909,0.0006982933,0.00203645,0.002067299,0.01364817],"category_scores_gemma":[0.001116998,0.0004122466,0.001272476,0.0005018636,0.000367674,0.0004820397,0.001037526,0.001591385,0.002428294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009373646,"about_ca_system_score_gemma":0.002084689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0198388,"about_ca_topic_score_gemma":0.02075401,"domain_scores_codex":[0.9997887,0.00004325339,0.000007526742,0.00002101611,0.00009664136,0.00004290124],"domain_scores_gemma":[0.9997591,0.00007973403,0.00001689143,0.0000391729,0.00006002723,0.00004493645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004049446,0.0002891358,0.001762655,0.0005222508,0.0002226234,0.000478927,0.0001761212,0.7631634,0.02454541,0.124739,0.04459992,0.03909562],"study_design_scores_gemma":[0.0001556551,0.00003192362,0.0003841475,0.00003155636,0.00001587556,0.00004367751,0.00001063763,0.97058,0.001584058,0.004997902,0.0221329,0.00003165677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1939194,0.004012981,0.6556284,0.003014894,0.001540499,0.001036046,0.01688116,0.01442833,0.1095382],"genre_scores_gemma":[0.437563,0.001776821,0.4963523,0.001119426,0.0003147799,0.003160823,0.0105966,0.004945406,0.04417089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0198388,"threshold_uncertainty_score":0.04565769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02688520047990929,"score_gpt":0.2939120788559905,"score_spread":0.2670268783760812,"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."}}