{"id":"W4220934394","doi":"10.1101/2022.03.22.485380","title":"The DNA-based global positioning system—a theoretical framework for large-scale spatial genomics","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science; Genome British Columbia; Burroughs Wellcome Fund","keywords":"Barcode; Genomics; Computational biology; Computer science; Pixel; Global Positioning System; Scalability; DNA sequencing; Optical mapping; Scale (ratio); Massive parallel sequencing; Spatial analysis; Artificial intelligence; Biology; DNA; Genome; Remote sensing; Genetics; Geography; Cartography; Gene; Database; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008376227,0.0005292324,0.0004277285,0.00004292092,0.0009291797,0.0003191006,0.00090784,0.0007898809,0.00002493416],"category_scores_gemma":[0.000208842,0.0005053388,0.0004176704,0.0001579113,0.0002228992,0.000004257104,0.0004759386,0.0006131293,0.000005994252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003895266,"about_ca_system_score_gemma":0.0008208157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002360521,"about_ca_topic_score_gemma":0.00001243083,"domain_scores_codex":[0.9971047,0.0002361965,0.000549409,0.001031009,0.0003324628,0.0007462379],"domain_scores_gemma":[0.9977217,0.0001031125,0.0003202481,0.001319083,0.00031534,0.0002205091],"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":[0.001467385,0.0005415994,0.00908056,0.0009720144,0.0006476064,0.00002939995,0.00001980324,0.002081449,0.8429433,0.1416751,0.0005301638,0.00001158007],"study_design_scores_gemma":[0.003953383,0.001094938,0.01222257,0.0007341643,0.0008863088,2.434731e-7,0.00009470209,0.02929977,0.8737311,0.0003042449,0.07425251,0.003426089],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.538901,0.001205261,0.4527748,0.0003218072,0.003096762,0.001187872,0.002330356,0.0001570604,0.000025125],"genre_scores_gemma":[0.9796303,0.00007771321,0.01781237,0.000417089,0.001369294,0.0005361252,0.00001625786,0.000138522,0.000002329227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4407293,"threshold_uncertainty_score":0.9997398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007334856423611699,"score_gpt":0.2204563786171767,"score_spread":0.213121522193565,"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."}}