{"id":"W7071865094","doi":"","title":"UNDERSTANDING HOW ACTIVINS CONTRIBUTE TO TGFß1 PROFIBROTIC SIGNALIING IN CHRONIC KIDNEY DISEASE","year":2023,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; University of Toronto; Chinese Academy of Sciences; Kidney Foundation of Canada","keywords":"Follistatin; Kidney disease; Context (archaeology); Fibrosis; Mediator; Transforming growth factor; Extracellular matrix; Receptor; Kidney","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":[0.00009773166,0.0003301801,0.000327375,0.0007385422,0.000218488,0.000306822,0.001239761,0.000234331,0.000609701],"category_scores_gemma":[0.00005931379,0.0003826337,0.0001188648,0.00244127,0.00003441572,0.000532539,0.0004181904,0.0003604176,0.00009996014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001890887,"about_ca_system_score_gemma":0.0006381591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008379692,"about_ca_topic_score_gemma":0.001450227,"domain_scores_codex":[0.9981365,0.00006122926,0.0001630609,0.0008190155,0.0002952477,0.0005250057],"domain_scores_gemma":[0.9987713,0.0000852041,0.0001558925,0.0006438954,0.00006748515,0.0002762201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004892653,0.0003203333,0.004255613,0.002233956,0.0005244351,0.002642924,0.004008703,0.003690144,0.007952786,0.6168042,0.01433688,0.3427407],"study_design_scores_gemma":[0.009745287,0.001249376,0.03068377,0.009845546,0.0006853109,0.00001505805,0.04250355,0.09662032,0.009426991,0.03785332,0.7528272,0.008544278],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0179972,0.0002879382,0.8473506,0.01200204,0.001300679,0.004623945,0.0002053112,0.003776091,0.1124562],"genre_scores_gemma":[0.6179721,0.0000748071,0.001931546,0.0001135131,0.0000665902,0.00004159674,0.0002116737,0.00007238762,0.3795157],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.845419,"threshold_uncertainty_score":0.9998626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03730838465503161,"score_gpt":0.2316251714245728,"score_spread":0.1943167867695412,"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."}}