{"id":"W4288481769","doi":"10.48550/arxiv.1903.01041","title":"Cell Density Quantification with TurboSPI: R2* Mapping with Compensation\\n for Off-Resonance Fat Modulation","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; Izaak Walton Killam Health Centre; Dalhousie University","funders":"","keywords":"Voxel; In vivo; Adipose tissue; SIGNAL (programming language); Biological system; In silico; Relaxometry; Chemistry; Nuclear magnetic resonance; Magnetic resonance imaging; Physics; Computer science; Biology; Artificial intelligence; Gene; Biochemistry","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":[0.002457662,0.001366274,0.000701951,0.001527628,0.0004360302,0.001222002,0.001908293,0.001389688,0.002013827],"category_scores_gemma":[0.003217334,0.0007056581,0.0006454459,0.001250171,0.0006419328,0.001566945,0.001194656,0.001306792,0.001106528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006666658,"about_ca_system_score_gemma":0.00136167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451796,"about_ca_topic_score_gemma":0.003825328,"domain_scores_codex":[0.9993541,0.0001626646,0.00003837561,0.0001339199,0.0002335938,0.00007739985],"domain_scores_gemma":[0.9989203,0.0002996216,0.0002019127,0.0002287696,0.0002569642,0.00009237549],"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":[0.000295296,0.0001377557,0.004644976,0.0004986834,0.0001231995,0.0003509964,0.0004822717,0.02551565,0.8429251,0.008176843,0.003932367,0.1129169],"study_design_scores_gemma":[0.00002602433,0.0001742019,0.003910099,0.00004821605,0.00008838729,0.000620636,0.00009330556,0.4658523,0.5147048,0.003087519,0.01124887,0.0001456082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0580948,0.0007578465,0.9352036,0.0003152838,0.00005923022,0.00009530941,0.0002214033,0.003509009,0.001743455],"genre_scores_gemma":[0.2110477,0.00115698,0.7815655,0.0002490194,0.00003529247,0.0004431523,0.000669275,0.001084207,0.003748933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002457662,"threshold_uncertainty_score":0.01299751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05525259026853475,"score_gpt":0.1742911585010934,"score_spread":0.1190385682325587,"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."}}