{"id":"W4309162880","doi":"10.1039/d2lc00798c","title":"Ultrathroughput immunomagnetic cell sorting platform","year":2022,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sorting; Computer science; Computational biology; Chemistry; Biology; Algorithm","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":[0.000521103,0.0004741035,0.0004158119,0.0007975909,0.0004747696,0.0006965433,0.0009269,0.0005969864,0.005187454],"category_scores_gemma":[0.0002695609,0.0002737264,0.0003492634,0.0003277271,0.0002423326,0.0003847356,0.0006698412,0.0006625787,0.003932265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004735531,"about_ca_system_score_gemma":0.0006705125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004902563,"about_ca_topic_score_gemma":0.0008691997,"domain_scores_codex":[0.9991447,0.00006482315,0.0000574195,0.0001813496,0.0004592625,0.00009240337],"domain_scores_gemma":[0.9997986,0.00004367879,0.00002584095,0.00003929556,0.00005485893,0.00003781977],"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":[0.00009328479,0.0001186468,0.0005587493,0.0001714265,0.00002951156,0.0001165596,0.00005803243,0.0009244765,0.9494179,0.003682126,0.009737701,0.0350916],"study_design_scores_gemma":[0.00003513396,0.0002515327,0.002215076,0.00003120398,0.00004568061,0.0005298672,0.00002712197,0.01540728,0.8337012,0.00113844,0.1465512,0.00006619946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1402469,0.004974488,0.7808081,0.002353777,0.001272884,0.0022294,0.01166043,0.01812847,0.03832554],"genre_scores_gemma":[0.3417613,0.005257988,0.5888354,0.002001253,0.0005045698,0.00445307,0.01465366,0.0004291922,0.04210343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005187454,"threshold_uncertainty_score":0.01735377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128452866591902,"score_gpt":0.185971559558851,"score_spread":0.174687030892932,"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."}}