{"id":"W2386383383","doi":"10.1186/s12989-016-0137-5","title":"Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials","year":2015,"lang":"en","type":"review","venue":"Particle and Fibre Toxicology","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"Health Canada","keywords":"Pulmonary disease; Transcriptome; Meta-analysis; Disease; Computational biology; Medicine; Computer science; Biology; Internal medicine; Genetics; Gene; Gene expression","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":[],"consensus_categories":[],"category_scores_codex":[0.0009262988,0.0002764747,0.002829109,0.0001914033,0.00007374371,0.00005961982,0.0002781149,0.0001239683,0.0004748915],"category_scores_gemma":[0.0002297151,0.0001966656,0.0009858268,0.0004004479,0.00008937974,0.00007643463,0.00006681323,0.00002957678,0.00008098831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002570168,"about_ca_system_score_gemma":0.0001373068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001138184,"about_ca_topic_score_gemma":0.000007478612,"domain_scores_codex":[0.9978224,0.0003636151,0.0008486189,0.0004871606,0.0001493164,0.0003288607],"domain_scores_gemma":[0.9981275,0.0007587101,0.0002390899,0.0004935277,0.0000756123,0.0003056001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.0005265372,0.0007690653,0.00006588671,0.004403421,0.03597221,0.00002213356,0.00052976,0.00005557287,0.9206254,0.004500055,0.0003997096,0.03213024],"study_design_scores_gemma":[0.0003089244,0.0002654851,0.0006910696,0.00008387898,0.6131185,0.00001368645,0.00005340526,0.0001397198,0.05549056,0.0007967855,0.3283199,0.0007181557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2016665,0.7937986,0.0001736778,0.0004625509,0.00009206887,0.001374116,0.002376668,0.00004866553,0.00000724186],"genre_scores_gemma":[0.656667,0.3308923,0.001999269,0.0003560227,0.000107069,0.008158715,0.0001425704,0.0001266534,0.001550415],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.8651348,"threshold_uncertainty_score":0.8019792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1997138666508429,"score_gpt":0.4110564777845816,"score_spread":0.2113426111337387,"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."}}