{"id":"W3088359255","doi":"10.1002/admt.202000575","title":"On‐Chip Rotation of <i>Caenorhabditis elegans</i> Using Microfluidic Vortices","year":2020,"lang":"en","type":"article","venue":"Advanced Materials Technologies","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto; University of New Brunswick","funders":"Canadian Institutes of Health Research; University of Toronto; State Key Laboratory of Robotics and System; Harbin Institute of Technology","keywords":"Microfluidics; Caenorhabditis elegans; Microscale chemistry; Microchannel; Confocal; Rotation (mathematics); Fluidics; Biological system; Microfluidic chip; Materials science; Controllability; Microscope; Fluorescence microscope; Nanotechnology; Fluorescence; Computer science; Optics; Biology; Physics; Artificial intelligence; Engineering","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":[0.00006833495,0.0001821692,0.0002454763,0.00004280138,0.00005945491,0.00002259823,0.0002932597,0.0001794704,0.00001489195],"category_scores_gemma":[0.0002658252,0.0001798997,0.00004519679,0.00009478996,0.0001341449,0.000009135825,0.000144279,0.00005926831,0.000007247308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008731556,"about_ca_system_score_gemma":0.00002863053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001153005,"about_ca_topic_score_gemma":0.000002071387,"domain_scores_codex":[0.9989843,0.00002846125,0.0003059436,0.0003526551,0.000110125,0.000218496],"domain_scores_gemma":[0.9993509,0.00001156393,0.0002068032,0.0003335157,0.00006937747,0.00002785005],"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":[0.00007182097,0.00002421046,0.0001085465,0.00006738194,0.00002249507,0.000002326079,0.00007776305,0.0003340162,0.9974372,0.000273987,0.0001853615,0.001394897],"study_design_scores_gemma":[0.0003134124,0.000433532,0.0001050166,0.00002329916,0.00002299162,0.000004197544,0.0002873347,0.00001239009,0.9956517,0.001517207,0.001437293,0.0001916157],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874598,0.001424627,0.01006169,0.0003953357,0.0001909844,0.00021979,0.00006726341,0.0001480432,0.00003243546],"genre_scores_gemma":[0.9894647,0.001185673,0.008915003,0.0002714211,0.00005424886,0.0000142549,0.00005407067,0.00003052799,0.00001012529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002004841,"threshold_uncertainty_score":0.7336099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272811583522511,"score_gpt":0.2395797646419288,"score_spread":0.2268516488067037,"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."}}