{"id":"W4376561104","doi":"10.1101/2023.05.11.540374","title":"Mapping cells through time and space with moscot","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Helmholtz Artificial Intelligence Cooperation Unit; German Network for Bioinformatics Infrastructure; Azrieli Foundation; Israel Science Foundation; Council for Higher Education; Bundesministerium für Bildung und Forschung; Joachim Herz Stiftung; Hebrew University of Jerusalem; European Commission","keywords":"Computer science; Computational biology; Biology; Context (archaeology); Chromatin; Scalability; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002367036,0.0005334991,0.0004403907,0.00008977828,0.0001409109,0.0001619632,0.0003621475,0.0005983919,0.0000108698],"category_scores_gemma":[0.00003783048,0.000531244,0.000101099,0.000221037,0.0001781774,0.000009194297,0.00039834,0.0004302403,0.00006009094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004178808,"about_ca_system_score_gemma":0.0002797141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001010321,"about_ca_topic_score_gemma":0.000008430118,"domain_scores_codex":[0.9977936,0.00007272166,0.0003072019,0.001104854,0.000225972,0.0004956886],"domain_scores_gemma":[0.9984667,0.00001959028,0.000202721,0.0009384212,0.0001979075,0.0001746443],"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":[0.00005221718,0.0000554192,0.001824633,0.000221515,0.000181873,0.00003078467,0.00001558678,0.0001409507,0.9966836,0.0000275417,0.0007651118,7.783102e-7],"study_design_scores_gemma":[0.0006052367,0.0001647855,0.005910328,0.000313143,0.00007453597,4.455006e-8,0.000005030471,0.0002759774,0.9761763,0.000002106231,0.0155961,0.0008763702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922641,0.0009882217,0.004921211,0.0002250591,0.0006331264,0.0005100068,0.0002267563,0.0002029974,0.0000285062],"genre_scores_gemma":[0.9865524,0.0009493763,0.01120494,0.0002496016,0.0005995316,0.00006697921,0.00000314951,0.0002295923,0.0001444319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02050724,"threshold_uncertainty_score":0.9997139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479979279522225,"score_gpt":0.2010364053138141,"score_spread":0.1862366125185919,"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."}}