{"id":"W4220686339","doi":"10.7554/elife.57393.sa2","title":"Author response: In vivo transcriptomic profiling using cell encapsulation identifies effector pathways of systemic aging","year":2022,"lang":"en","type":"peer-review","venue":"","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"","keywords":"Profiling (computer programming); Effector; Transcriptome; Computational biology; In vivo; Encapsulation (networking); Gene expression profiling; Cell biology; Biology; Chemistry; Computer science; Gene; Gene expression; Genetics","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":[0.00264389,0.0005893568,0.0005480782,0.0006749993,0.0008964336,0.001896398,0.0006183989,0.002425211,0.1771743],"category_scores_gemma":[0.01416794,0.0002184723,0.0002949,0.0005002798,0.000694147,0.001056082,0.001392674,0.001846277,0.0779935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008463641,"about_ca_system_score_gemma":0.002346312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008917,"about_ca_topic_score_gemma":0.003350975,"domain_scores_codex":[0.9981241,0.0002795397,0.0001124801,0.0003103428,0.0009966253,0.0001769655],"domain_scores_gemma":[0.9852298,0.002549458,0.0006240748,0.0007549124,0.008819355,0.002022351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000366336,0.00003721886,0.001010038,0.0004247915,0.00001692598,0.0004531761,0.0002422543,0.0002971799,0.01788073,0.001651532,0.9284536,0.04916609],"study_design_scores_gemma":[0.00004616941,0.00008338152,0.002138728,0.00009822872,0.00001265129,0.0002631855,0.0004427317,0.000327589,0.009392265,0.0009260834,0.9862344,0.00003463969],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.03256097,0.007266114,0.02170985,0.361693,0.3125628,0.00144415,0.009393486,0.007627153,0.2457426],"genre_scores_gemma":[0.05580447,0.003818962,0.006362367,0.02838238,0.01853844,0.0002518624,0.003761982,0.00139918,0.8816803],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1771743,"threshold_uncertainty_score":0.592707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03204371720616132,"score_gpt":0.2905395551710807,"score_spread":0.2584958379649193,"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."}}