{"id":"W3049183505","doi":"10.1016/j.jcis.2020.08.052","title":"Nucleation and growth of cholesteric collagen tactoids: A time-series statistical analysis based on integration of direct numerical simulation (DNS) and long short-term memory recurrent neural network (LSTM-RNN)","year":2020,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université du Québec","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; McGill University","keywords":"Nucleation; Statistical physics; Materials science; Phase diagram; Population; Phase (matter); Soft matter; Thermodynamics; Condensed matter physics; Physics; Chemical physics; Chemistry; Colloid; Quantum mechanics; Physical chemistry","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":[0.0005570095,0.0001251785,0.0003657959,0.0002010265,0.0001219956,0.0001641222,0.0001172045,0.00004082549,0.00009559659],"category_scores_gemma":[0.0003440864,0.0001017353,0.00004360058,0.0008633313,0.0002899418,0.0007865375,0.00004631548,0.00007524085,6.734552e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004272188,"about_ca_system_score_gemma":0.00008097664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003887243,"about_ca_topic_score_gemma":0.000003455029,"domain_scores_codex":[0.9985164,0.0001301696,0.0005510012,0.0002327394,0.0004398178,0.0001298572],"domain_scores_gemma":[0.9987661,0.0001609638,0.0004872585,0.0000773265,0.0003564015,0.0001519027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007458155,0.00006193178,0.008565859,0.00004313708,0.00001604417,0.000001048124,0.0007538233,0.01431689,0.9727534,0.00002403132,0.00002171237,0.002696296],"study_design_scores_gemma":[0.0004069012,0.001554945,0.2687993,0.0001259451,0.0001742288,0.000010703,0.0001975869,0.5848642,0.1436859,0.00001186514,0.00001835007,0.0001500399],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9577111,0.00005956862,0.04163458,0.0002170027,0.0001468855,0.0001405151,0.000020066,0.000007997714,0.00006226426],"genre_scores_gemma":[0.9988011,0.0000446413,0.001017662,0.00006158238,0.00005185843,0.000001207054,0.000003428865,0.000005852916,0.0000126843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8290675,"threshold_uncertainty_score":0.4148646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551425504380834,"score_gpt":0.2790694147112519,"score_spread":0.2635551596674436,"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."}}