{"id":"W2587441226","doi":"10.1182/blood.v106.11.1074.1074","title":"Fully Automated Magnetic Labeling and Separation of Hematopoietic Cells from Multiple Samples.","year":2005,"lang":"en","type":"article","venue":"Blood","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Terry Fox Research Institute; Stemcell Technologies","funders":"","keywords":"Automation; Biomedical engineering; Magnetic separation; Separation process; Haematopoiesis; Cord blood; Computer science; Chromatography; Stem cell; Chemistry; Materials science; Immunology; Biology; Engineering; Cell biology","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":[0.001044968,0.0006164006,0.0004617392,0.001013956,0.0005132601,0.0007423529,0.0007330732,0.0005863575,0.002119456],"category_scores_gemma":[0.0008471201,0.0004885088,0.0003944615,0.0004506589,0.0004082207,0.0004297755,0.0009146176,0.0008638276,0.001712857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003529091,"about_ca_system_score_gemma":0.0006617819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007186055,"about_ca_topic_score_gemma":0.001556826,"domain_scores_codex":[0.9986612,0.0001469549,0.00008421976,0.0002865571,0.0007101148,0.0001109833],"domain_scores_gemma":[0.9994305,0.0001325392,0.0001053991,0.00008507671,0.0001913786,0.00005513843],"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":[0.00007455304,0.00005045551,0.0004497357,0.00009064253,0.00001267613,0.00005656998,0.0000462545,0.000232169,0.9647929,0.0003152688,0.001003694,0.03287518],"study_design_scores_gemma":[0.00005217721,0.0002871099,0.005538227,0.00002917821,0.00003551627,0.0007399332,0.00003175398,0.009543175,0.9621511,0.0006573916,0.020896,0.00003847945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2405941,0.002784236,0.7343394,0.0006800482,0.0003459881,0.001727574,0.002631578,0.007583733,0.009313396],"genre_scores_gemma":[0.2446046,0.001081693,0.739743,0.0004355242,0.0001427428,0.001140629,0.003815485,0.0004351128,0.008601188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002119456,"threshold_uncertainty_score":0.007090271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009982188043345535,"score_gpt":0.2246532018129394,"score_spread":0.2146710137695939,"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."}}