{"id":"W2939354810","doi":"10.7554/elife.45239","title":"Opto-magnetic capture of individual cells based on visual phenotypes","year":2019,"lang":"en","type":"article","venue":"eLife","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Genome Canada; Canadian Institutes of Health Research; Canadian Cancer Society; Fonds de Recherche du Québec - Santé","keywords":"Population; Confocal; Cell sorting; Biology; Phenotype; Cell biology; Isolation (microbiology); Nanotechnology; Biophysics; Cell; Materials science; Physics; Bioinformatics; Gene; Genetics; Optics","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":[0.00009343784,0.000121631,0.000133461,0.00003272121,0.00002030659,0.00001169378,0.0001676976,0.0001421986,0.0001322062],"category_scores_gemma":[0.00001460317,0.0001103533,0.00007961632,0.00005044053,0.00003864837,0.000001358389,0.00002584477,0.00008142016,0.0000510227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004114016,"about_ca_system_score_gemma":0.00006006414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001927864,"about_ca_topic_score_gemma":0.000006989727,"domain_scores_codex":[0.9992379,0.00003262985,0.0001435901,0.0002206003,0.0002096344,0.0001556252],"domain_scores_gemma":[0.999621,0.00001412448,0.00004711761,0.0002155061,0.00004897395,0.00005321579],"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":[0.0001395983,0.000159963,0.00468885,0.00002799033,0.00001801305,8.623683e-7,0.00005787534,0.0006263976,0.9916152,0.00001866257,0.001165251,0.001481295],"study_design_scores_gemma":[0.0009124223,0.0009623,0.003413533,0.00001892002,0.0000203336,5.034187e-7,0.00003753658,0.0007324921,0.9788697,0.00000315368,0.01485538,0.0001737456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961851,0.0003010962,0.000256533,0.00004276462,0.000332087,0.0001358175,0.00003793113,0.000008965129,0.002699744],"genre_scores_gemma":[0.9976771,0.00001665401,0.000474804,0.0009476714,0.0001312275,0.000003032406,0.00009606258,0.0000196805,0.0006337455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01369013,"threshold_uncertainty_score":0.4500076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006167996849397467,"score_gpt":0.216341107462654,"score_spread":0.2101731106132565,"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."}}