{"id":"W2007063717","doi":"10.1007/s10439-013-0791-9","title":"Automated Micropipette Aspiration of Single Cells","year":2013,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":94,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipette; Micromanipulator; Biomedical engineering; Position (finance); Viscoelasticity; Computer science; Materials science; Computer vision; Artificial intelligence; Engineering; Chemistry; Composite material","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":[0.00006263059,0.00006291398,0.00009109968,0.00005529435,0.000008266004,0.000005148519,0.00007353359,0.00008051726,0.0000710302],"category_scores_gemma":[0.00005148325,0.00006063196,0.00006334003,0.00007922628,0.00002120235,0.000004147458,0.00003370276,0.00003514785,0.000008817154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001893084,"about_ca_system_score_gemma":0.00001171692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002665319,"about_ca_topic_score_gemma":4.506335e-7,"domain_scores_codex":[0.9995071,0.000007050456,0.0002003749,0.0000944534,0.00008225172,0.000108796],"domain_scores_gemma":[0.9996612,0.000007269978,0.00005857767,0.0001236301,0.00009332434,0.00005599207],"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":[0.000002079159,0.00006408935,0.000001659104,0.00002678174,0.0000262781,2.633999e-7,0.000007556835,0.0001029031,0.9947587,0.00004079748,0.003450513,0.001518409],"study_design_scores_gemma":[0.00007750421,0.0002533031,0.00006630578,0.00002316444,0.000003339513,0.000001770824,0.000006407333,0.01640211,0.9651449,0.000007781973,0.01795424,0.00005911998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767151,0.000234875,0.02232007,0.0002409914,0.0002295729,0.00008575952,0.00001248833,0.00002624172,0.0001348355],"genre_scores_gemma":[0.9985762,0.00002949949,0.001120405,0.00007923828,0.00006323903,0.000004549277,0.00005673331,0.000009703226,0.00006042916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02961371,"threshold_uncertainty_score":0.24725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141875976898479,"score_gpt":0.2421822997167601,"score_spread":0.2279947020269122,"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."}}