{"id":"W3019798172","doi":"10.1038/s41598-020-62837-8","title":"A 96-well culture platform enables longitudinal analyses of engineered human skeletal muscle microtissue strength","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; St. Michael's Hospital; Canada's Michael Smith Genome Sciences Centre; Occupational Cancer Research Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada First Research Excellence Fund; Government of Canada; Ontario Institute for Regenerative Medicine; Krembil Foundation; Heart and Stroke Foundation of Canada","keywords":"Skeletal muscle; Myogenesis; Muscle contraction; Myocyte; Contraction (grammar); Biomedical engineering; Biology; Computational biology; Medicine; Cell biology; Anatomy; Internal medicine","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":[0.0006230208,0.0002088967,0.000322999,0.0001956951,0.0001499288,0.0001710994,0.000326856,0.0001230548,0.0006732423],"category_scores_gemma":[0.0003584064,0.0001822014,0.0001542706,0.00101843,0.0002998731,0.0001719304,0.0001535323,0.0003171802,0.00004928015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005064414,"about_ca_system_score_gemma":0.0000588262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003898426,"about_ca_topic_score_gemma":0.00001006358,"domain_scores_codex":[0.9974295,0.0000165592,0.0006375667,0.0005662566,0.0008629051,0.0004871898],"domain_scores_gemma":[0.998777,0.0000386685,0.0001137051,0.000573068,0.0001601744,0.0003373837],"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":[0.000001672139,0.00005309159,0.001105023,0.0004034458,0.00009237546,0.0003737981,0.0006321516,0.002306816,0.9646037,0.00003334466,0.0281426,0.002252041],"study_design_scores_gemma":[0.0002229847,0.00008173913,0.003442337,0.0001157254,0.00004701212,0.00005539266,0.0001994375,0.006839909,0.8802042,0.000407971,0.1080124,0.0003709489],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933929,0.0006434825,0.0009383794,0.00006102186,0.001184476,0.0002442057,0.0000107083,0.0002959633,0.003228799],"genre_scores_gemma":[0.9972655,0.00001147364,0.00167364,0.000004895161,0.0001457949,0.000009109902,0.00009336852,0.00003073644,0.0007654478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08439946,"threshold_uncertainty_score":0.7429957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05199229868277865,"score_gpt":0.3167841572105374,"score_spread":0.2647918585277588,"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."}}