{"id":"W4320881663","doi":"10.1101/2023.02.15.528626","title":"High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Traction (geology); Microscopy; Materials science; Planar; Fluorescence recovery after photobleaching; Tractive force; Biological system; Biomedical engineering; Optics; Physics; Fluorescence; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002712061,0.0003795535,0.0002297668,0.0004194223,0.0002248014,0.0003944445,0.0003989483,0.0005248827,0.001127652],"category_scores_gemma":[0.0003154522,0.0001864715,0.0001349861,0.0002076297,0.0003918241,0.0005301104,0.000530746,0.0003899844,0.0004293138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004544171,"about_ca_system_score_gemma":0.0002577276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001105802,"about_ca_topic_score_gemma":0.001529234,"domain_scores_codex":[0.9998637,0.00001484094,0.000006492683,0.00003066331,0.00006657987,0.00001772753],"domain_scores_gemma":[0.9998705,0.00004287829,0.00002643744,0.00002073267,0.000025201,0.00001424401],"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":[0.00001413641,0.000008808706,0.0001894322,0.00002026552,0.000002653086,0.00002688527,0.00001335751,0.0008979444,0.9953594,0.0004483439,0.00008710205,0.002931834],"study_design_scores_gemma":[0.000009681515,0.00002617147,0.002329,0.000006720569,0.000004569867,0.0001526441,0.00002255455,0.05915618,0.9356595,0.0006179723,0.001996517,0.00001852223],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.687183,0.0009558845,0.3067867,0.0002623252,0.00006294985,0.0000602638,0.000624486,0.000727254,0.003337132],"genre_scores_gemma":[0.7758112,0.0008160433,0.2184649,0.00006581818,0.00003762408,0.00008517481,0.000523438,0.0001836267,0.004012212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001127652,"threshold_uncertainty_score":0.003772378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198879301494658,"score_gpt":0.271772428121418,"score_spread":0.2518844979719522,"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."}}