{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000144668,0.0002752988,0.0001930738,0.0001546734,0.0001737511,0.0002268858,0.000391021,0.0001541846,0.000670975],"category_scores_gemma":[0.0001661704,0.0002025869,0.0001644841,0.00007044526,0.0002427905,0.0001866545,0.0002977889,0.0001815345,0.000160798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002486517,"about_ca_system_score_gemma":0.0003078364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006972423,"about_ca_topic_score_gemma":0.00105519,"domain_scores_codex":[0.9998993,0.000009069084,0.000006900329,0.00003521832,0.00002304187,0.00002647978],"domain_scores_gemma":[0.9999077,0.00001940978,0.00003221214,0.00001383465,0.00001189073,0.00001490173],"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":[0.00003594581,0.00001454371,0.0002480698,0.00002577584,0.000003636618,0.00002218455,0.00001281831,0.0003353809,0.9943337,0.0001734924,0.0001643389,0.004630084],"study_design_scores_gemma":[0.0000258031,0.000157432,0.002020534,0.000004482777,0.000008537245,0.00005231796,0.00001391131,0.00641744,0.9882231,0.00003750757,0.003017581,0.00002129439],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697897,0.0004068831,0.02631286,0.0001615749,0.0001072524,0.00009058624,0.0002646928,0.0006019886,0.00226447],"genre_scores_gemma":[0.9729866,0.0003087789,0.02460534,0.00004858142,0.00002376537,0.0000892265,0.0001927006,0.00002788482,0.00171712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006972423,"threshold_uncertainty_score":0.002244592,"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."}}