{"id":"W2919175161","doi":"10.1101/567685","title":"Methods for Analyzing Imaging Mass Cytometry Data with Tellurium Probes","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Connaught Fund; Natural Sciences and Engineering Research Council of Canada; University College London","keywords":"Tellurium; Mass cytometry; Resolution (logic); Chemistry; Biological system; Computer science; Biology; Artificial intelligence; Biochemistry","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":[0.00528203,0.001813204,0.001406841,0.004549093,0.00102099,0.003352677,0.002714761,0.001293519,0.0177351],"category_scores_gemma":[0.01599363,0.001703888,0.00139378,0.003230912,0.001689402,0.002139703,0.002563877,0.003575665,0.01235056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033563,"about_ca_system_score_gemma":0.001415359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006261306,"about_ca_topic_score_gemma":0.001017609,"domain_scores_codex":[0.9953223,0.0009241381,0.000607155,0.0009116911,0.002034746,0.0001999981],"domain_scores_gemma":[0.9920524,0.003465473,0.0009943439,0.001853262,0.001431181,0.0002033177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004850024,0.0002231744,0.003453929,0.00205573,0.0003622624,0.0005714837,0.0006987629,0.009021114,0.4206578,0.105356,0.02261354,0.4345013],"study_design_scores_gemma":[0.0002015328,0.0002636783,0.005466838,0.0003728076,0.0001427292,0.001622278,0.0002538594,0.2055316,0.4771856,0.1219804,0.1865748,0.0004038723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007837953,0.000109535,0.9946046,0.00008135123,0.00005910757,0.0001335665,0.0003724856,0.003250649,0.0006048215],"genre_scores_gemma":[0.007527392,0.0003016204,0.9870859,0.0001007916,0.0000563839,0.001458607,0.0008182244,0.001136798,0.001514267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0177351,"threshold_uncertainty_score":0.05932981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02277168858323918,"score_gpt":0.279846866854937,"score_spread":0.2570751782716978,"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."}}